Facilities Location for Residual Biomass ... - ACS Publications

Research Article Cite This: ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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Facilities Location for Residual Biomass Production System Using Geographic Information System under Uncertainty José E. Santibañez-Aguilar,* Antonio Flores-Tlacuahuac,* Francisco Betancourt-Galvan, Diego F. Lozano-García, and Francisco J. Lozano Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Campus Monterrey, Ave. Eugenio Garza Sada 2501 Sur, Col. Tecnológico, Monterrey, Nuevo León 64849, Mexico S Supporting Information *

ABSTRACT: Biomass is a renewable resource that has attractive characteristics for manufacturing many types of products such as biofuels, specialty chemicals, and energy generation. Several studies have addressed diverse important features such as new technologies for biomass treatment, approaches for potential location of refineries, models to evaluate existing relationships between availability and price, among others. However, two important issues need to be considered: potential locations for biomass processing systems facilities considering geographic constraints, as well as uncertainty associated with biomass availability over time. Thus, this paper presents an approach to determine viable facility locations for supply chains based on residual biomass. This work has used a tool based on geographic information systems in order to involve environmental, social and geographic restrictions. Some of considered environmental features are exclusion of protected areas, vegetation type according with human activities, and water bodies location. Also, the approach considers technical constraints given by the terrain slope, wetland zones, distance to transportation infrastructure, distance to power grids and power plants, etc. In addition, social constrains such as distance to urban and rural communities, and historic monuments are included as well. Additionally, climatic aspects such as frequency of drought and hurricanes have been also taken into account. Besides, this work has considered the effect of biomass uncertainty through several scenarios, based on historical data for residual biomass availability, to include variations over time of this factor on biomass processing systems. The proposed geographic information system was tested for a nationwide case study in Mexico to determine viable regions to locate biomass processing systems facilities. The results show that biomass availability variations over time due to uncertainty have a strong effect on viable regions to install facilities in a supply chain based on biomass. Furthermore, the results also illustrate the influence of the type of raw material on the number of potential locations. This method is useful to define potential locations to be taken into account in problems like supply chain design or planning problem because it is possible to decrease considerably the number of options to choose accounting social, environmental, and geographic issues as well as uncertainty associated with biomass availability. KEYWORDS: Geographic information system, Agricultural residues, Uncertainty, Sustainability dimensions

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INTRODUCTION Energy production around the world is based mainly on fossil fuels. These energy sources generate large amounts of greenhouse gas emissions, which are associated with the global warming problem. Lundgren et al.1 stated that it is necessary to limit greenhouse gas emissions due to human activities. In fact, some authors have proposed alternatives to address this issue, mainly involving renewable energy sources, in addition to carbon capture and sequestration. In this regard, biomass is a renewable resource that has gained the attention of society and scientific community. For instance, Hong et al.2 mentioned that biofuels are a sustainable way to fulfill global energy demand. Additionally, Ng et al.3 have quoted that biomass has high potential to be used as renewable energy source through several technologies. Agricultural products are used as feedstocks for human and livestock, some crops generate large amount of residues and their © XXXX American Chemical Society

price is generally lower, which can be used to produce energy or chemicals. Table 1 shows ratios for residue production per crop amount as well as potential energy generating via ethanol production. It is important to note that for some raw materials the residue production is larger than the main crop production. Also, it can be noted that for several residual biomass types, the produced energy via ethanol production is lower than the generated energy via direct combustion of agricultural residues (approximately 10000 kJ/kg). Thus, Aldana et al.4 concluded that Mexico’s residual biomass can be used to produce energy, and should be taken into account in a national energy policy. Received: September 29, 2017 Revised: January 9, 2018 Published: February 6, 2018 A

DOI: 10.1021/acssuschemeng.7b03303 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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ACS Sustainable Chemistry & Engineering Table 1. Potential To Produce Energy from Some Residual Biomass Types via Ethanol Production

Main crop Corn Wheat Rice Barley Sorghum Sugar cane Agave Pecan Nut a

Agricultural residue

Residue amount per crop amount

Corn stover Wheat straw Rice straw Barley straw Sorghum straw Sugar cane bagasse Agave residue Pecan nut shell

(

kg of residue kg of crop 52

)

Produced ethanol per crop amount

(

kg of ethanol kg of crop 53

)

Produced ethanol per residue amount

(

kg of ethanol kg of residue 53

)

Energy produced (ethanol combustion) per residue amount

(

0.825 1.83552 1.62552 1.66054 1.42554

0.3149 0.285753 − − 0.299953

0.3817 0.155753 − − 0.210453

10226 4171 − − 5638

0.825a

0.059253

0.071853

1922

0.11155

0.04056

0.36256

9698

0.55052

−

−

−

kJ kg of residue

)

Supposed to be equal than corn.

environmental objectives. For the case study, authors illustrate the spatial distribution of the annual yields of the three major biomass resources in 102 counties. Also, Yue et al.9 developed a mixed-integer nonlinear programming formulation to address optimized operational decisions, as well as profit allocation mechanisms in supply chain optimization. Gonela et al.10 presented a model able to consider uncertainties in a supply chain for bioethanol production involving environmental, economic and social aspects. Frombo et al.11 proposed a decision support system for planning a system based on energy production from biomass. Santibañez-Aguilar et al.12 presented a mathematical programming approach to obtain a set of products (ethanol, energy, acetic acid, etc.) from water hyacinth through a distributed biorefinery network. Additionally, Murillo-Alvarado et al.13 proposed a model for optimal planning of a supply chain using agave residues to produce ethanol. Santibañez-Aguilar et al.14 presented an approach for optimizing a supply chain for production of biofuels considering environmental, economic and social objectives. Also, Aldana et al.4 presented a model to select a set of technologies and locations for processing plants and consumers to produce energy from residual biomass through diverse technologies. Shabani et al.15 presented a study for a supply chain configuration based on forest biomass for power generation. Moreover, Yue et al.16 proposed a stochastic robust optimization model to handle both strategic and operational supply chain uncertainties in a holistic framework; they show the differences in the optimal supply chain layout from robust solution, deterministic solution and standard stochastic programming solution for Illinois. Yue et al.17 proposed a comprehensive multiobjective life cycle analysis framework to quantify both direct and indirect environmental impacts to incorporate these impacts into the decision making process, their framework was applied on the sustainable design of a potential bioethanol supply chain in UK. As seen, several works have focused in determining locations, raw materials, processing technologies for biomass processing system; nevertheless, previous works do not accomplished a financial analysis to determine the invesment risk to promote these types of processing systems. To address this drawback, SantibañezAguilar et al.18 presented an approach to obtain the financial risk due to implementing a biomass processing system. Additionally, previous works have presented methodologies for optimal selection of facility locations for biomass processing. However, initial locations are chosen by authors based on their own criteria, neglecting important aspects such as climate

In this respect, using residual biomass allows that crops main produce is used as human and livestock feed, not generating deviation from their main purpose and possible price increase. Therefore, a processing system based on residual biomass can be an attractive alternative to obtain economical and environmental benefits. The economical benefits is associated with a low cost for residual biomass whereas environmental benefits are due to a considerable reduction in the greenhouse gas emissions, mainly CO2. In order to provide a context for residual biomass availability susceptible to be processed further, Table 2 shows Table 2. Crop Production and Estimated Residues Generated for 2014 in Mexico Year 201441 Crop

Production (ton)

Value (103 Mexican pesos)

Agave Rice Sugar cane Barley Corn Pecan nut Sorghum Wheat

2408884 232159 56672,829 845707 23133599 122536 8394057 3971536

10137225 921449 26225927 2950771 72077147 6106022 19986210 12644957

the agricultural products for crops like corn, sorghum, sugar cane, wheat, barley, agave rice, and pecan nut in Mexico for 2014, as well as the estimated residual biomass. Those crops represent the largest amount produced in the country. Additionally, to design a biomass processing system, it is crucial to determine potential locations to install supply and processing centers. Yue et al.5 stated that several of these challenges (including selection of feedstocks and harvesting sites, as well as assortment of products, processing facilities and potentials consumers) can be addressed through mathematical programming approaches for planning of supply chains. Accordingly, several approaches have addressed the supply chain design problem based on biomass conversion. For instance, You et al.6 proposed a model for optimal design of a supply chain based on ethanol production from geographically distributed cellulosic biomass considering economic, environmental and social criteria. You et al.7 developed a mathematical approach for the optimal planning of a supply chain to produce liquid biofuels considering a multisite distributed and centralized supply chain for liquid transportation fuel. More recently, Yue et al.8 presented a life cycle optimization framework for distributed supply chains introducing the concept of functional unit for economic and B


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consider potential changes in the biomass availability and a base value was considered. Sahoo et al.33 developed an integrated GIS system to identify optimal plant sites and to calculate delivery costs. Their work used three primary sustainability indicators such as soil erosion, soil conditioning index and crop production greater than a lower limit. They applied an exclusion-inclusion method to determine plant locations. Sharma et al.34 conducted a spatial exclusion and preference GIS analysis for a bioetanol plant siting. They considered several layers such as road and railroad for transportation, terrain slope, water wells and rivers, natural gas pipelines and natural protected areas. However, the work considers the available biomass variability only for spatial analysis (different in each regions), but not variations in the biomass availability for the same site. Zhang et al.35 realized a work focused on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost using an integrated GIS approach. In this respect, they used the GIS based methodology to preselect potential biofuels facility locations. Information layers for the GIS approach were county boundaries, a railroad transportation network, a state/federal road transportation network, water body dispersion, city and village location, a population level, biomass production, and location of cofired power plants. In this case, the biomass availability is supposed to be known for each location, but the biomass availability is unknown because its production is associated with uncertainty due to climatic phenomena and projections in land change. Finally, Zhang et al.36 developed an approach to combine Geographic Information System based analysis with an optimization modeling method. The GIS-based approach was developed alike to that of Zhang et al.35 to obtain candidate locations served as inputs for optimization modeling. In this case, the supplies with abundant biomass production is preferred to locate the sites. However, the selection of abundant sites is only based on the biomass availability and not on the associated uncertainty. However, crop yields vary through time due to several reasons: rain fallout, natural disasters such as drought incidence, hurricanes, frost and hailstorms, pests, market price fluctuations, making farmers not plant in some cases. This generates uncertainty for residual biomass estimation. Then, it should be highlighted that in most of previous works the impact of uncertainty over biomass facilities locations has not been systematically addressed. Also, relevant constraints linked to social and environmental issues have been neglected. In addition, geographic constraints linked to climatic phenomena have not been included in previous approaches. Therefore, this paper proposes a model based on GIS to determine viable regions to locate biomass processing systems facilities considering uncertainty related to agricultural residues availability, constraints related to environmental, social and economic impact, as well as climatic phenomena effects. It should be noted that the main difference is that conventional analysis considers that attribute in information layer is already known. For instance, locations and extensions for highways and roads, population size, location for cities and towns are established. However, the present manuscript takes into account that the amount of available residual biomass is unknown. In fact, the uncertainty analysis is based on the assumption that municipalities with high available raw material amount depend on different predictions. This methodology provides viable locations for installing biomass suppliers and processing facilities in a robust form

conditions, distance to road and highways, or terrain conditions, which can cause that an optimal location given by previous methodologies renders a nonviable site in a real case study. Besides, the number of initial options (number of locations, products, biomass types, etc.) is directly related to problem size, because many options can demand large CPU time, or even that a feasible solution cannot be found. Hence, a selection method can help to improve the inputs to these methodologies in order to reduce the number of options and avoid choosing nonviable locations. Geographic Information Systems (GIS) have been applied for systematic location of residual biomass processing systems to manufacture biofuels or other products. In fact, according to Sharifzadeh et al.,19 the implementation of strategies for planning biomass supply chains should consider several aspects as biomass availability, which is subject to seasonal and geographical factors. GIS are composed by geographic data and advanced software to manage and show this information in a descriptive map (see ref 20). Some examples for GIS use in biomass processing system are discussed in several works. For instance, Graham et al.20 proposed an approach to use grasslands taking into account raw material price, transportation cost and biomass availability. Also,21 developed a scheme to produce ethanol from grasslands considering biomass availability and price as well as roads and highways location. Laasasenaho et al.22 established a 50 km radius around biomass producer regions to install power plants. Similarly, Vukasinović,23 considered installing processing plants near high consumption zones. Additionally,24 used GIS to locate potential biomass suppliers included natural protected areas, soil carbon content and erosion. Moreover, Fiorese et al.25 provided a scheme for bioproducts (products from biomass) production from residual biomass; this analysis was based only in bioresource availability. Furthermore, there are works based on GIS that have considered specific parameters. For example, Haddad et al.26 used a conceptual model based on GIS to assess potential biomass supply sites considering bioresource availability, terrain slope level, road and highways, restricted land and distance to water sources. Zhang et al.27 proposed a model to process biomass in small quantities; this work took into account that a population level greater than 1000 inhabitants can ensure workforce. Moreover, Brahma et al.28 proposed a production system based on residual biomass considering distance between harvesting sites and urban communities as the main parameter to be considered. Finally, Ma et al.29 and Sultana et al.30 considered criteria such as terrain slope, distance to highways, distance to electrical infrastructure, restricted regions, inhabitants number, and distance to water sources. Additionally, there are recent papers focused on location of biorefineries. For instance, Gonzales et al.31 proposed an interesting method to locate biorefineries and depots considering the incidence area of a biorefinery in the terrain to locate the depots. Their paper does not consider any information layer associated with the environmental impact or social effect such as the water bodies location or distance to communities. Their selection is based on available biomass and biorefinery size. In this regard, the biomass available is assumed constant for each one of predictions and each scenario is treated individually. Martinkus et al.32 presented a biorefinery siting tool incorporating site-specific biogeophysical measures. It should be noted that their work includes social and economic criteria such as social capital index, human capital index and electricity rate. In addition, they have considered a methodology based on weights for the information layers. Nevertheless, this study does not C


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layers can be used to obtain a set of locations inherently more viable from environmental, social and economic points of view. For instance, Medina-Herrera et al.39 presented an study for distillation system design inherently safer before to a rigorous system design.

because it takes into account diverse important features linked to geographic constraints, where obtained results in this approach can be useful as input information for a more complex processing biomass system, such as, optimal planning and scheduling of supply chains, determining of viable raw materials, locations, processing plants locations, among others. Hence, the proposed approach aims to include four features: economic, environmental, social and crops season. For instance, crops season effect is taken into account through uncertainty in biomass availability because available raw material is different for each period of time (year, month, day, etc.). Uncertainty analysis in agricultural residues availability involves generating several scenarios, which can be associated with raw material availability in different periods of crops season. Consequently, our approach considers the impact caused by variations over time on biomass availability. The impact of the environmental factor is considered by exclusion of natural protected areas, wetland zones and water bodies. Also, to reduce environmental damages, the proposed GIS model includes exclusion of regions that have not been affected by human activities. Social issues are accounted for through exclusion of historic and archeological sites. Another social impact is given by adequate distance to urban and rural communities to promote jobs generation and to avoid negative effects in normal society activities. Economic dimension is taken into account via exclusion or inclusion of infrastructure for transportation and power grids. Moreover, terrain slope affects the economic performance because this is related to facility location and transportation costs. Finally, this work has included some climate risks constraints such as frequency of drought and hurricanes. Application of the proposed approach is illustrated through a case study to municipal level in Mexico, although it might be applied to other countries or different case studies.

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GIS CONSTRUCTION AND ANALYSIS A GIS system is composed by an integrated collection of geographic data, as well as the proper software to manage and display this information. In addition, a system based on GIS is able to realize spatial analyses and relating this information with other geographic data to generate new geographic information layers. Moreover, a GIS system is able to classify geographic information based on diverse attributes. In summary, a GIS system has the ability to perform tasks utilizing spatial data and attributes associated with them, which is a distinctive characteristic of a GIS system because other information management systems do not relate their information to spatial data. It should be noted that there are several software packages for GIS systems such as ArcGIS37 (from Esri), QGIS38 (open source software) and Mapa-Digital-Mexico40 (from Mexican government). In this work, we use the ArcGIS software package to approach the optimal location issue of biomass facilities. Commonly, when running a GIS analysis one first defines a set of attributes such as biomass availability, transportation infrastructure, connection to power lines, etc., to name just a few attributes. Hence, S1 , S2 , ... ., Sn ∈ < stand for sets featuring different attributes, n is the number of such attributes and < is a universal set representing all potential elements of such sets. In fact, the elements of set Si = {s1,s2,..., sm} actually consists of spatial coordinates of the i-th attribute, i.e. sk = {xk, yk, zk}, where m stands for the cardinality of set Si and x, y, z are the familiar spatial coordinates. From a sets theory point of view, a GIS system computes the set Ŝ representing the intersection of the sets S1, S2,..., Sn:

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PROBLEM STATEMENT The proposed approach is a GIS based conceptual model that takes into account diverse geographic features and uncertainty related to amount of available raw material to validate potential locations for residual biomass suppliers and processing plants in biomass processing systems. Therefore, the addressed problem can be defined as follows. Given the following information: • Amount of biomass available for different years in order to carry out uncertainty analysis. • Geographic environmental restrictions such as natural areas, water bodies and wet zones exclusion and distances to them. • Geographic and social constraints associated with locations to human communities, historic monuments and archeological sites. • Distance to highways, power grids, airports and rails as well as terrain slope. • Geographic data related to climatic risk such frequency of drought, hurricanes, hailstorm and frost. Then, the problem consists in determining viable locations for biomass processing systems facilities taking into account the above set of constraints through specialized software (such as ArcGIS37 or QGIS38) for analyzing, viewing and editing geospatial information. Regarding a quantitative analysis for addressing social or environmental impact, it is necessary to establish that the aim of this paper is not to accomplish a quantitative analysis for these impacts. However, we consider that the proposed information

S ̂ = S1 ∩ S2 ∩ ... ∩ Sn

(1)

Hence, in a typical GIS system the elements of the resultant set Ŝ are graphically represented, which quickly allows one to assess the quality of the resultant location for the intended aims. Because information in graphical rather than in tabular form can be easier to grasp, GIS systems represent an easy and convenient framework for stakeholders job. Following, we explain the performance of a GIS system using the set of attributes defined in this work. The proposed GIS implementation consists of determining the geographical area influenced by the various information layers being considered. Each layer has different influence in residual biomass suppliers selection and in the location of processing facilities. Besides, in the present work it is necessary to distinguish between locations and areas. Areas are determined by the GIS model taking into account suitable constraints, whereas locations are specific sites where facilities may be installed. Facility locations can be obtained in several ways. They can be determined considering one facility per each location. Another way to locate facilities might be the viable regions size, considering upper and lower limits for facilities size. For instance, facilities can be located considering a ratio of 10 km (314 km2) up to 50 km (7854 km2) around facilities. This paper has considered a potential location per each remaining municipality. Raw Material Availability. Raw material availability is one of the key issues to be taken into account for designing any D


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Figure 1. General representation of the proposed methodology for potential locations of biomass processing facilities.

larger number of scenarios. However, including many scenarios can result in excessive computational load. We have observed that there are a typical number of scenarios beyond which GIS results do not change significantly. This observation was used in the present work to fix the number of suitable scenarios for proper uncertainty representation. Figure 1 illustrates the general structure for the proposed methodology. Figure 1(a) shows representation for different scenarios for raw material availability level, where each scenario corresponds to diverse biomass inventories, locations and year season. These locations are spatially intersected to find common locations with high biomass availability level. It is worth noting that Figure 1 is a schematic representation for the proposed methodology. In this regard, each prediction for biomass availability represents different municipalities with high biomass availability. Furthermore, each municipality has different geographic extension. This means that a given scenario can represent 100 municipalities with a defined area, whereas another scenario can also contain 100 municipalities but with different area. For that reason, if the municipalities are different for each predictions, then the represented area is different too. Based on historic data provided from official sources,41 average and standard deviation for the amount of available biomass is obtained. It is important to highlight that standard deviation is a

supply chain system, because raw material availability affects directly the configuration and supply chain total cost. This GIS constraint is related to the economic performance of a production system, because if any facility accounts for large amount of available raw material, then transportation costs are inherently lower. It is noteworthy that residual biomass availability level changes over year season and this is affected seriously by external factors such as climate phenomena. For that reason, bioresource availability should be taken into account in a supply chain design problem. It is important to note that values for biomass availability for future years are unknown, but agriculture residue availability could be estimated from reported data in previous years. Accordingly, agricultural residue availability was obtained from official sources41 at the municipal level. Associated uncertainty due to biomass availability was addressed through the scenarios approach, where each scenario represents a potential prediction for annual biomass availability (for each municipality and biomass type). Therefore, the number of scenarios is related to the number of potential options for biomass availability. Hence, if the scenarios number is small, then only a small portion of the full uncertain space for biomass availability values would be represented. On the other hand, better representation of uncertainty can be achieved using a E


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Figure 2. Generation of the amount of biomass for each scenario and a specific biomass type: Using average and standart values for the 2009−2014 cycle, and the Latin Hypercube sampling method, N possible predicted values of the available biomass are obtained.

measure used to quantify dispersion of a data set. A low standard deviation indicates that the data points tend to be close to the mean of the set, whereas a high standard deviation indicates that the data points are spread out over a wider range of values. For this reason, the values of mean and standard deviation are useful to calculate upper and lower limits for diverse predictions for biomass availability. Hence, the lower limit is equal to the average biomass production minus standard deviation, whereas the upper limit is equal to the average biomass production plus standard deviation. Then, several random biomass availability predictions are realized, where biomass availability lies between upper and lower bounds. All biomass availability predictions are constrained in order to produce values for biomass availability similar to near past years. Scenarios were generated through a Latin Hypercube sampling method (see Figure 2) because this sampling method produces a representative distribution with a relative small scenarios number (see ref 42). The Latin Hypercube sampling method is a sampling method able to ensure that the ensemble of random numbers is representative of real variability. Before to apply the method, the number of samples points, S, as well as the range of each variable, V, should be defined. Then, the range of each variable is divided into S equally probable intervals. It should be noted that the number of sample points, S, is equal for each variable to satisfy the Latin Hypercube requirements. Thus, a matrix arrangement of V × S columns by V × S rows is done. It should remarked that for each sample point it is necessary to record the row and column the sample point was taken from. This sampling method allows obtaining a uniform distribution of the uncertain parameters (raw material availability) along the full uncertain space; however, any another distribution method could be used. This way, possible changes over year season for raw material availability are taken into account. It is important to note that each prediction of biomass availability is lie the upper and lower bounds. Therefore, all biomass availability predictions are limited in order to produce values for biomass availability similar to near years. As previously stated, each scenario represents a different availability level for each municipality and biomass type. Additionally, two municipalities selection processes were developed to choose viable municipalities for each scenario. Figures 3 and 4 show schematic representations to describe both municipalities selection processes. In the first selection process, municipalities are sorted in descending order according to their biomass availability level for a given scenario. Then, municipalities with larger availability raw material level are selected such

Figure 3. General algorithm for first municipality selection process (based on biomass availability). The procedure is illustrated for only one scenario, but it should be repeated for all scenarios.

that sum of available biomass for selected municipalities represents 80% of total available raw material for that scenario. Subsequently, this selection procedure is applied to all other scenarios. Besides, in the second selection process, the ratio between the municipal biomass availability and municipality area is computed for all municipalities and for all scenarios. Then, municipalities are sorted in descending order based on the aforementioned ratio. Afterward, municipalities with larger values of that ratio are selected such that sum of available biomass for selected municipalities is at least 80% of total available biomass for that scenario. Similarly, this second selection process is applied to all other scenarios. Therefore, the viable municipalities (based on raw material availability) correspond to the selected municipalities through these two selection methods. It should be noted that each selection algorithm selects a set of municipalities in which the sum of municipal available biomass is at least 80% of total available biomass for each scenario. It should be stressed that 80% for biomass availability was chosen arbitrarily, but any other value can be assumed. Nevertheless, this target was considered as F


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candidate to be selected. Also, if we want to select a higher amount of “good” municipalities, then uncertainty analysis could take into account a lower percentage to met; for example, 70% instead of 80%. The first selection criterion for municipalities promotes one to choose municipalities with high residual biomass availability while municipality size is neglected. The second selection criterion takes into account municipality sizes. This explains why the number of selected municipalities using the second criterion is larger than for the first criterion. Thus, the second municipality selection strategy promotes social development in small communities (low community size and low biomass availability level, but high biomass availability level per area unit). Social development is promoted because facility location can generate new jobs and useful infrastructure like highways and markets near to facilities. Besides, it should be noted that selected municipalities are different for each scenario. For that reason, each set of selected municipalities (one set for each scenario) represents a different geo-spatial region, which is a region with high biomass availability. Subsequently, the spatial intersection of all scenarios using ArcGIS 10.4 is computed. Then, the set of chosen locations is equal to the common space for all scenarios. In summary, the intersection of the scenarios represents the most viable locations independently of year season. This procedure excludes potential locations with low or inconstant raw material production. Geographic Constraints Nonassociated with Raw Material Availability. Moreover, a set of viable locations is obtained by geo-spatial intersection of viable locations associated with each one of the stated criteria (water bodies, transportation, climate conditions, etc.). A general representation of the way these viable locations are obtained is depicted in Figure 1(b). Finally, in Figure 1(c) the locations from Figures 1(a) and 1(b) are intersected to produce a set of viable locations and biomass availability level to install biomass processing systems facilities. In order to provide a detailed description and justification of the geographic constraints considered in this work, each one of them are described in next sections. In addition, several maps for the influence of information layers over the study area are provided as Supporting Information. Terrain Slope. Terrain slope was provided by a Digital Elevation Model (DEM), which is an important parameter to be considered in processing plants and residual biomass suppliers location because this parameter can affect the feasibility to install

Figure 4. General algorithm for second municipality selection process (based on biomass availability per area unit). The procedure is illustrated for only one scenario, but it should be repeated for all scenarios.

adequate value because it represents the most probable level of the available raw material. Additionally, Figure 5 shows a single example to clarify both selection methods. It is worth noting that the selection procedures are also a limitation for the present work. Nevertheless, the proposed uncertainty analysis is focused on the selection of the “best” municipalities featuring high biomass availability for any biomass type. In this regard, the sampling method used in this work employs standard deviation to avoid choosing “bad” candidates and to give preference to “good” candidates with high residual biomass availability. Thus, if for any biomass type and any municipality the standard deviation is large, then this match (biomass type and municipality) would result in a “bad”

Figure 5. Representation for municipalities selection methods. G


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environmental indicators such as Eco-Indicator99.46 Herein, the use of primary vegetation can affect seriously the ecosystem in regions with minimum human impact, which can be more drastic than in regions where previous human activities have been done. Because these regions are the habitat for several species of animals and plants, it is worth noting that most of previous works in literature have not considered this set of geographic restrictions. Distance to Urban and Rural Communities. Social issues are considered in the proposed GIS model through rural and urban communities population levels. This way, distance to urban and rural communities can feature negative and positive social impacts. For example, if facilities are located too far, social impacts will be negative due to jobs sources, whereas, if facilities are close to communities, then the normal activities of communities can be modified featuring negative social impact. It should be noted that people activities might be affected by the change in the city metabolism, because the vehicles flow can increase drastically. Also, city sources can be used to supply the facilities, which may cause negative effects too. According to the literature, there are several limits for distance from cities to facilities. Thus, the lower bound depends on population limits. The lower limits are established as follows: • 500 m for 500−1000 inhabitants • 1000 m for 1000−2000 inhabitants • 2000 m for 2000 inhabitants • 2000 m for large urban communities

supply chain facilities (supplier centers, distribution centers and consumption regions), as well as transportation costs. Sites with large slope level might cause expensive construction. Furthermore, the access to sites with large terrain slope is more difficult and terrain slope is directly related to fuel consumption. Huertas et al.43 have used hilly terrain as an important factor to calculate fuel consumption in a route for transportation. In the research literature, different slope level classifications to be accounted in the geoprocessing model have been proposed. For instance, Demek et al.44 stated that a slope level lower than 15% would imply high feasibility level. DEM data was obtained from refs 40 and 45. Data were classified with respect to slope level according to ref 44 (a terrain slope lower than 15% is associated with high viability). It is worth noting that this information layer allows eliminating a large nonfeasible country spatial region because of mountain regions and canyons. Transportation Infrastructure. This GIS constraint is related to transportation cost for raw material and products, as well as labor workforce. For that reason, transportation infrastructure, such as distance to roads and railways, also should be included in the facility location analysis. This study considered only interstate highways, state highways and railways. Short distances to roads and highways permit quick access to residual biomass and products transportation to processing plants or consumers. According to the literature,26 a radius of up to 24 km to highways, roads and railways promotes easy access to supply chain facilities because a truck traveling at 60 km/h could take approximately 30 min to pick up and deliver products and raw material. Additionally, another distance can be considered to avoid affecting this infrastructure, which is recommended to be equal to 30 m. Therefore, a highly viable region based on highways, roads and railways is larger than 30 m but lower than 24 km. Also, location near airports and heliports should be avoided. In fact, a minimum distance of 800 m around them should be enforced. Power Grids and Power Plants. Processing facilities should be located within suitable distance from useful industrial infrastructure such as electrical power grids and power plants because building new power infrastructure is expensive. To provide feasible limits in information layers for facility location, we based these limits on diverse references in the literature. The recommended distance from electrical infrastructure to ensure high feasibility level is at least 100 m because lower distance can cause alterations and negative effects on infrastructure. However, for this case, we have not considered an upper bound of distance from power plants. Although, viable area for locations is actually limited by others information layers. Water Bodies, Natural Protected and Nonviable Areas. To decrease environmental damage upon selection of potential facilities location, the proposed GIS model takes into consideration exclusion of some locations. An adequate distance to water bodies is associated with a positive environmental impact. If the distance between facilities and water bodies is adequate, then the effect over water bodies can be minimized. Hence, locations within 800 m radius from permanent and 100 m radius from intermittent water bodies should be avoided. Also, this approach considers exclusion of locations that should be protected due environmental regulations like natural protected locations that are within 800 m radius from potential locations. Additionally, it is important to note that any human activity causes an impact on the environment as measured by different

whereas the upper bound is 3000 m for any inhabitants level (see refs 30 and 27). It is worth noting that, in order to promote positive social impact distance from communities, they have to feature a medium value between lower and upper bounds. Exclusion of Restricted Land. There are locations that should be restricted because of municipalities policies or country permits. This part of our GIS approach includes a geographic layer with historic monuments location and archeological sites. No biomass processing facilities should be located within a 800 m radius from such sites. It is worth noting that the size of this area is not large compared to the corresponding area considering other constraints. Nevertheless, laws to use this area are very strict and the use of this land may cause a negative social impact. Risks for Raw Material Availability Associated with Climate Conditions. The proposed GIS approach has considered the risk associate to raw material availability dictated by external factors. In this regard, some of the main aspects that can affect raw material availability, as well as required transportation infrastructure, are climate conditions. For instance, when phenomena such as hurricanes occur, this may affect raw material availability and some regions may be non accessible. The conceptual model presented in this work considers the risk for drought, frost and hailstorm as well as hurricanes frequency to account for risk associated with weather conditions. Therefore, a location with high risk of drought, frost and hailstorm features low viability to install any supply chain facility. Also, high hurricane frequency affects negatively facilities location. For that reason, regions with high incidence of the aforementioned phenomena have to be excluded. We have several information layers for Mexico climatic phenomena. For this case, we have found information about climatic phenomena reported by the Mexican Disaster Prevention Center (CENAPRED). This institution has carried out diverse studies to determine diverse regions with very high, high, medium, low and very low risk for drought, frost, hailstorm and hurricanes. H


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Figure 6. Comparison of biomass availability scenarios number and municipality selection method effects over locations for sorghum straw.

time and in general cannot be assumed as constant. Additionally, values for biomass availability for future years are unknown, but these values could be estimated from reported data in previous years. Using information on biomass availability for the 2009− 2014 period,41 50 scenarios for biomass resources were forecast. This means that the recorded information was discretized using 50 scenarios for one year. Scenario generating procedure is described in the GIS Construction and Analysis section. The sampling method was implemented in the MATLAB environment, where the CPU time for generating 50 scenarios for 2446 municipalities was around 5 min for each one of the accounted raw materials. Resultant scenarios for each raw material and municipality are provided in theSupporting Information. Once scenarios for biomass availability were generated, this information was linked to geographic constraints through GIS software. The software for representing raw material information data scenarios was ArcGIS 10.4. This software is a powerful tool to represent information concerning geographic locations as well as to compute diverse ”operations” among them. It should be stressed that each scenario features different amount of biomass for each municipality and crop type. In fact, each scenario considers the total number of municipalities (2446). Additionally, two different municipalities selection processes (described in previous section) were carried out and applied to each scenario to determine the municipalities for facilities location. The first selection process is based on available biomass amount for each municipality, whereas the second selection process is based on biomass amount per area unit (where this area is given by municipality size). It is worth noting that the location for raw material availability depends on the number of scenarios, biomass type and municipality selection method. Figures 6 and Figure S1 in the Supporting Information illustrate locations for two biomass types (sorghum straw and sugar cane bagasse), three scenarios featuring different number of discretization points (10, 30 and 50) and the two selection criteria (based on availability level and availability level per area

Geographic data can be found in the Web site given by ref 47. In addition, a brief description about how these risks were obtained is provided: • Drought: This phenomenon has been analyzed by Escalante-Sandoval et al.48 Analysis consisted on an individual assessing for each municipality taking into account rain deficit and duration. • Hurricanes: In this case, the probability density function was used considering the frequency and strength for hurricanes at municipal level. Also, the social vulnerability index and the marginalization index was used to calculate hurricanes impact. • Hailstorm: The number of days per year with hailstorms is used according with Mexican National Risk Book. • Frost: The number of days with frost at municipal scale considering previous years is used. The days with frost is normalized taking into account the maximum value of days with frost for the considered years. Also, for this index, the social vulnerability index and the marginalization index was used. As can be seen, GIS constraints are based on exclusion or not exclusion of some regions to determine viable sites. However, information layer for biomass availability is obtained through a previous uncertainty analysis; which includes a methodology to select the municipalities with high biomass availability.

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RESULTS AND DISCUSSION The GIS approach was implemented in a nationwide case study for Mexico to obtain the best potential locations for biomass facilities that hold several constraints. The addressed case study comprises several agricultural residues such as corn stover, sugar cane bagasse, sorghum straw, agave residue, wheat straw, rice straw, barley residue, and pecan nut shell. This approach takes into account the uncertainty effect regarding available biomass. Uncertainty stems because the amount of biomass changes over I


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ACS Sustainable Chemistry & Engineering Table 3. Summary of Municipality Selection by the Uncertainty Analysis First reduction process

Second reduction process

Cultivate name

Base year (2014)

Remaining municipalities

% Reduction

Remaining municipalities

% Reduction

Corn stover Rice straw Agave residue Sorghum straw Sugar cane bagasse Wheat straw Barley straw Pecan nut residue

2334 83 249 630 266 528 222 159

276 15 9 66 68 17 25 14

88.2 81.9 96.4 89.5 74.4 96.8 88.7 91.2

634 25 25 144 110 50 68 27

72.8 69.9 90.0 77.1 58.6 90.5 69.4 83.0

Figure 7. Viable and nonviable locations considering the most of GIS restrictions. (a) Terrain slope and biomass availability not included. (b) Terrain slope included but biomass availability not included.

As can be seen, there is not a method to determine the optimal number of scenarios to represent uncertainty related to biomass availability. However, when the number of scenarios is enough, then the expected availability or the expected municipalities with high biomass availability are reached. Thus, the optimal number of scenarios is the minimum number of scenarios such that the number of municipalities with high availability keeps constant. For instance, the number of locations for 10 scenarios is greater than the number of locations for 30 scenarios. Nevertheless, the number of locations for 50 scenarios is almost equal to the number of locations for 30 scenarios. This explains why 50 scenarios were used for crop data discretization. Also, Figure 6 shows that the number of municipalities depends upon the reduction method. The first reduction process (based on raw material availability level) selects a lower number of municipalities than the second area reduction process (based on raw material availability per area unit). For example, Figure 6 depicts that the number of municipalities when 50 scenarios are considered is equal to 66 for the first case, whereas for the second reduction process is equal to 144. This indicates that the second area reduction method selects a larger number of municipalities. Figure S1 in the Supporting Information presents the same analysis than the one shown in Figure 6 but for sugar cane bagasse. It can be noted that the number of municipalities is similar to the past case. Nevertheless, sugar cane bagasse dependence with respect to number of scenarios is lower than the case for sorghum straw. This behavior can be explained noticing that the sugar cane bagasse producer locations are very specific

unit). Figure 6 shows locations for sorghum straw using the two selection algorithms. It can be noted that, if the scenario number increases, then the total number of locations decreases. However, the number of locations turns out to be constant if the number of scenarios is adequate (the number of scenarios is enough to represent the full uncertain space). In order to explain this behavior, it should be stressed that each scenario represents different values for biomass availability for each municipality. Subsequently, municipalities with major available biomass are selected according with 2 selection criteria (see Raw Material Availability section). Therefore, each scenario is associated with a different area or set of locations with high biomass availability. It should be noted that these areas are based on the municipalities size. Furthermore, although each scenario represents a different selected area (area with high biomass availability), there is a common area in which all areas from predictions are equal. This means that there is a set of municipalities with high amount of available biomass, which is independent of the scenario or prediction for the raw material availability. Thus, if the number of scenarios increases the common area, in which all predictions are coincident, is reduced. Nevertheless, the final area cannot be fully reduced, because there are municipalities with high amount of available biomass. For that reason, when scenarios increase, the selected area is the common area for all scenarios or equal to a constant area. This means that the final selected area tends to be equal to the expected municipalities area with high amount of available biomass. J


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K

Municipal 47 Regions with very high and high risk or severity has excluded to available risk to install any facility

40

National and municipal National

Risk associated with climate conditions

Natural Resources, protected and nonviable areas Urban and rural communities Restricted land

This layer has the function of excluding area occupied by current urban and rural communities This layer has the function of excluding prohibited area; such as, historic and archeological sites areas This layer has the function of excluding area with high risk of raw material availability due to climate conditions

It is recommend at least 500 m for communities with more than 500 inhabitants, 1000 m for communities with more than 1000 inhabitants and 2000 m for communities with more than 2000 inhabitants It is recommend at least 800 m from the prohibited sites.

40

National

National 40

40, 57, 58 It is excluded the area where water bodies, and vegetation are located as well as a ratio of 800 m around them

100 m × 100 m National 40, 45 40, 45

Values for slope lower or equal than 15% is associated with high viability It is preferred a distance lower than 24 km and greater than 30 m. Also it is preferred a distance greater than 800 m from airports and other facilities It is preferred a distance of at least 100 m

High terrain slope level affects the installation viability It is an indirect way to measure the viability to transport raw material and products This layer promotes a quick access to this important infrastructure This layer should be avoided to decrease the ecosystem damage

41 Several scenarios were supposed and intersected to obtain the best spatial region independent of year To consider sites with high raw material availability

Residual Biomass Availability Terrain Slope Transportation Infrastructure Electrical Infrastructure

Description Layer Name

Table 4. Information Layers for the Proposed GIS Approach

Consideration

Source

Scale

and they have few variations regarding residual biomass availability. Additionally, the uncertainty analysis was run for all accounted raw materials. Table 3 presents the selected municipalities according to the uncertainty analysis. The second column shows the number of agricultural residues municipalities for 2014. The third column contains the resultant number of municipalities for each raw material using the first municipality selection method. The fifth column presents the number of municipalities for each crop type using the second selection method. It can be noted that the inclusion of uncertainty in raw material availability reduces significantly the number of municipalities. It is important to stress that municipalities with larger percentage reduction are municipalities with larger variation in raw material availability. Regarding the selection methods, the first method considers the total biomass availability for municipalities. However, the limitation of this method is that it does not take into account the area effect over biomass availability, because a larger municipality can have a larger amount of available biomass (i.e., a big municipality could have a lot of available surface), but biomass can be dispersed in this municipality and the transportation costs and consumed energy can be increased. On the other hand, the second method considers biomass availability per area unit. Therefore, in the second method biomass cannot be dispersed because this method selects small municipalities with high local biomass availability. In consequence, selected municipalities in the first method are lower than those selected using the second method. Therefore, if the objective is reduction in the options to future applications, the first method is preferable. However, if the objective is to minimize the recollection cost and promote social development, the second method is preferable. Furthermore, facility locations based on different information layers (terrain slope, climate conditions, highways, urban and rural communities, water bodies, etc.) can be obtained. Figure 7 displays locations after applying the geographic, technical and environmental constraints previously mentioned (see GIS Construction and Analysis section). Figure 7 consists of two parts: the first one shows locations considering all constraints except the terrain slope. The second one displays locations when the terrain slope is included. Table 4 presents a summary of constraints that should be taken into account for biomass facilities location. According to Figure 7, constraints have significant influence on the chosen region because a large proportion of the total available region (total country area) can be removed. Moreover, terrain slope is an important constraint because by enforcing it, a large portion of locations can be eliminated. The reason why terrain slope eliminates a large number of locations is because Mexico features mountain landscape. Further, locations obtained by geographic constraints as well as raw material availability under uncertainty are intersected using the ArcGIS 10.4 software. It should be remarked that there are two locations obtained by raw material availability constraints because two municipalities reduction methods were used (see uncertainty analysis). Following, results for intersection of aforementioned information layers are presented in diverse figures. Figure 8 depicts locations for installing biomass suppliers or processing facilities for corn stover. The results from the uncertainty analysis are one of the reasons why some municipalities were nonselected, when all constraints are applied. Nonselected municipalities decrease approximately 50% when all constraints are applied regarding the resultant area from uncertainty analysis. It should be noted that the number of

Municipal



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Figure 8. Viable and nonviable locations for corn stover and different municipality selection methods.

Furthermore, for the barley straw and pecan nut shell cases, the GIS method led to large regions as a consequence of the uncertainty analysis, which may suggest a small variation in biomass availability. However, those regions were almost completely excluded, when other geographic constraints were applied. The most severe case corresponds to wheat straw crop, because only 17 municipalities resulted from uncertainty analysis for the first municipalities selection method. Nevertheless, only one municipality was chosen when all geographic constraints were applied. This indicates that the use of sorghum straw and sugar cane bagasse have moderate risks as raw material in a processing system. In contrast, barley straw, pecan nut shell and wheat straw as raw materials can be associated with high risk (only a few options are feasoble). Although, these raw material may be used in production processes, assuming that their use is justified by others economic or environmental studies such as presented by Santibañez-Aguilar et al.14 Furthermore, Tables 5 and 6 present a summary of locations when all biomass types and location selection methods are considered. The lowest reduction percentage corresponds to sugar cane bagasse (58.6% for first selection method and 69.2% for second selection method). In comparison, the highest reduction percentage is associated with wheat straw with 99.8% for first selection method and 96.6% for second selection

reported municipalities includes any municipality with at least a small portion of viable area. Figures 9 and 10 illustrate viable locations for rice straw and agave residue, respectively. These figures show that geographic constraints do not affect seriously regions obtained from uncertainty analysis (scenarios intersection) for these raw materials, because the number of locations are almost the same before applying the full set of geographic constraints. This behavior can also be observed from Table 5 for the first municipality selection method, because the reduction percentage caused by geographic constraints by comparison with municipalities from uncertainty analysis was 13.3% for rice straw and 11.1% for agave residue. These percentages for the second selection method were 20% for both raw materials (see Table 6). Figures S2, S3, S4, S5 and S6 in the Supporting Information depict viable locations for sorghum straw, sugar cane bagasse, barley straw, pecan nut shell and wheat straw, respectively. For these residual biomass types, geographic constraints have an important effect over the locations. For instance, using sorghum straw the number of municipalities decrease approximately 50%, with respect to municipalities obtained from uncertainty analysis. In addition, for the sugar cane bagasse the percentage of biomass is reduced from 73% down to values around 50% (47% for first and 55% for second municipalities selection method). L


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Figure 9. Viable and nonviable locations for rice straw and different municipality selection methods.

locations to install supply chain facilities for residual biomass use. In addition, it is important to mention that the original motivation for this work was based on talks with Mexican ́ government (Secretariá de Energia/Energy Department). In essence, they are looking for tools for the development of a manufacturing system based on biomass renewable energy to determine (a) locations where biomass can be processed, (b) economically relevant biomass types to obtain biofuels, (c) risk associated with biomass processing and (d) selection of routes and products to get the best benefits. Thus, the present approach is part of a more complex project. For that reason, we have limited the geographic analysis to a boolean analysis to simplify the subsequent problems. Although, we have not discarded to evaluate geographic layers with different weights. In addition, the general idea is that a biorefinery could be installed anywhere within the viable region; although, a variable location within a region can be considered (see refs 49 and 50) as potential sites for facilities installation. For this case, the biorefinery size is an important factor to be considered. For that reason, we have established a minimum size of viable region to install a supply chain facility. This size was predefined as 314 km2 (a circle with radius of 10 km2) for small facilities and 7854 km2 (a circle with radius of 50 km2) for large facilities. This way, if there is a viable area lower than 314 km2, then the facility instalation is considered to be infeasible.

method. It is worth noting that locations obtained using the second selection method produce a larger number of them. Nevertheless, both methods produce important reduction of ”the initial locations” compared with ”the final locations”. For that reason, both options are powerful strategies to reduce the number of locations in processing systems. Finally, diverse regions have been identified in Figure 8 and Figures S2 to S6 in the Supporting Information to provide a general idea of viable zone size for each one of the crops. At this point, it is necessary to highlight that the potential biorefinery is not located at the centroid of a municipality. Each selected municipality has a final viable region. In optimization models, we use the selection of potential locations, obtained from GIS analysis, as input information to a kind of superstructure options from which the final biorefinery location will be obtained. Thus, the resulting region is the region to be used to install the facility from which transporting and energy costs can be obtained to determine the final supply chain configuration. In fact, if a part of a given municipality is not viable, then this area is considered infeasible for the design of the supply chain. Also, it is important to mention that overlay of Boolean maps is not novel in the field of GIS based decision modeling. Nevertheless, it is a useful tool to determine if locations are viable or nonviable for a set of criteria. It is worth noting that the main objective of the paper is determining the “best” potential M


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Figure 10. Viable and nonviable locations for agave residue and different municipality selection method.

Table 5. Summary for Municipalities Selection by First Municipalities Reduction Method Cultivate name Corn stover Rice straw Agave residue Sorghum straw Sugar cane bagasse Wheat straw Barley straw Pecan nut residue

Resultant after uncertainty analysis

Resultant after geographic constraints

% Reduction uncertaintygeo

% Reduction from total

276

153

44.6

93.4

15 9

13 8

13.3 11.1

84.3 96.8

66

24

63.6

96.2

68

47

82.3

58.6

17

1

94.1

99.8

25

2

92.0

99.1

14

3

78.6

98.1

Table 6. Summary for Municipalities Selection by Second Municipalities Reduction Method Cultivate name Corn stover Rice straw Agave residue Sorghum straw Sugar cane bagasse Wheat straw Barley straw Pecan nut residue

Resultant after uncertainty analysis

Resultant after geographic constraints

% Reduction uncertaintygeo

% Reduction from total

634

331

47.8

85.8

25 25

20 20

20.0 20.0

75.9 92.0

144

75

47.9

88.1

110

82

25.5

69.2

50

18

64.0

96.6

68

21

69.1

90.5

27

8

70.4

95.0

In order to provide a clearer idea about the scope and limitations of the approach the main limitations of the manuscript are discussed: • Uncertainty due to input data accuracy and updating, uncertainty of model itself: In order to reduce this uncertainty, we have used official and government sources.

Table 7 presents the region size for each one of identified regions. Regions with at least 314 km2 are marked as “S” because these regions are viable to install small facilities, regions with at least 7854 km2 are marked as “L&S” and regions with size lower than 314 km2 are marked as “N” because the size of these regions is not enough to install large/small facilities. N


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ACS Sustainable Chemistry & Engineering Table 7. Size of Selected Regions From Figures 8 to 10 and S2 to S6 First reduction method Cultivate Name

Region number

Area (km2)

Install

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1

6594 30419 489 3316 1432 10244 1716 1566 329 3987 7772 4626 2566 865 492 342

S L&S S S S L&S S S S S S S S S S S

Barley straw

1 2

406 207

S N

Pecan nut shell

1 2

2659 2226

S S

Corn stover

Rice straw

Agave residue

Sorghum straw Sugar cane bagasse Wheat straw

Second reduction method Region number

Area (km2)

Install

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2

3819 2441 1970 1848 373 1191 1975 342 1012 2757 1533 192 1797 1452 2563 1789 308 80 142 733 106 507 1388

S S S S S S S S S S S N S S S S N N N S S S S

•

•

It should be noted that information layers such as city locations, road and highways locations, water bodies locations, or power grids locations do not feature important uncertainty when these layers are compared to biomass availability. In this regard, we have considered the effect of uncertainty over biomass availability via generation of multiple prediction of biomass availability. Notice that LHS was selected to ensure a representative distribution of the sampled variables. • Use of a boolean model instead a weighted method: The proposed approach is based on a boolean model using the “and” operator for viable locations selection. In consequence, the present approach has a limitation due to the fact that several “good” candidates can be excluded a priory. However, we consider that this approach is adequate because the aim of this paper is to select only the “best” locations according with the proposed criteria. Hence, “good” locations are always worse than ”best” locations. The logic behind that choice is that the supply chain planning problem is very complex and there are other aspects that have to be considered in future works such as demand uncertainty, supply chain dynamic behavior and control, multiobjective optimization (profit, environmental impact, social impact, multistakeholder scheme). It is worth noting that all aforementioned problems are more complex if the options number increases. In this regard, we have tried to simplify the first part of the addressed problem for eliminating most of potential viable options. Nevertheless, an approach about a weighted method for the spatial analysis is very interesting and we have though to apply it in future works. • Overlay approach: The proposed approach is based on overlay information layers. In this regard, the proposed

•

•

O

approach is a tool for determining potential viable locations for installing a supply chain facility. In this context, the chosen locations might feature major biomass availability and inherently avoid negative social and environmental impacts. Nevertheless, the amount of available raw material is not limited to these specific locations because the selected locations always can use biomass from their neighbors if there is easy access to them. Combination of different scales: The scale and resolution of the data was one of the most important limitations because Mexico is a big country (1.964 × 106 km2). Data for biomass availability were obtained from municipal scale, whereas others were obtained for specific locations in shape format. It should be noted that the number of polygons were very large, and the operations between them were simplified through sets theory. Terrain slope was obtained in raster format in scale 100 m × 100 m. In this respect, the different problem scales were addressed by spatial analysis in order to choose potential viable sites. Lack of true social criteria: the objective of this paper is not assessing social or environmental impact for location of supply chain facilities. Although, the methodology used in our work might be applied to evaluate social impact. The objective of this paper is determining viable sites to install supply chain facilities, which would be inherently more friendly with environment and society, although the impact on these variables was not addressed. It should be noted that the most of previous approaches for optimal supply chain planning do not consider any selection method for initial locations to obtain the optimal supply chain topology. Thus, this paper is focused to improve the selection methods for initial locations in these kind of problems. However, this is a limitation of the paper. Use of the model as a preselection procedure: In fact, the proposed model can be used as a preselection procedure for more complex problems associated with the optimal supply chain planning and scheduling in order to determine the supply chain topology, met demand, net annual profit, or environmental impact. In this respect, the proposed approach is limited because it is not able to determine the interactions between the supply chain nodes, it is not capable to compute the overall profit or environmental impact. However, this can be useful to generate the input data for these more complex problems. In addition, for all of these mentioned problems (planning and scheduling), “good” inputs are necessary and the proposed approach provides “inherently viable” options for the next step in the design problem. Determination of potential sites instead of the exact facility location: The reason why the final established locations are not the exact biorefinery location is that there are other aspects to consider prior to make a final decision. For example, the interactions between depots, processing facilities, retailers and consumers. Moreover, the aim of the addressed geographic information system is just to establish some of the most promising facility locations. Later on, based on this set of facility locations, an optimization problem would be formulated to choose, among those promising locations, one of the best ones based on a given objective function. An interesting research problem, in which we are working now, would consist in the simultaneous determination of the DOI: 10.1021/acssuschemeng.7b03303 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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viable locations, because the reduction of viable options was greater than 80% for most of biomass types. This denotes a significantly problem size reduction. Additionally, the proposed methodology was applied to several biomass types. Thus, results for multiple feedstocks can be observed if results for each one of considered raw materials are compared each other, in order to analyze viable sites where two or more raw materials can be feasible. Regarding seasonal biomass availability, it has not been considered in this work. Nevertheless, seasonal biomass availability analysis could be done from the uncertainty analysis. Also, this approach has shown that for some raw materials such as corn stover the number of selected locations is too large. This indicates that the locations for corn stover can be refined including a restriction based on sizes for viable locations. In contrast, other raw materials such as wheat straw and barley straw are associated with high risk level because the number of locations is very limited. Moreover, it is worth noting that there are others layers or restrictions that can be included such as local regulations or government permission or land owners willing to use their land for processing facilities or harvesting. Nevertheless, consideration of these constraints perhaps would imply a multistakeholder scheme because of terrains where processing plants or harvesting sites are installed can belong to two landlords or two municipalities with different local regulations. It should be noted that this work has not included this detail level; although, inclusion of these layers can enrich the social aspect of future contributions. Finally, this methodology has obtained feasible locations when all constraints are met. However, it is a limitation of the present work because it is important to stress that in some cases holding all constraints might be not possible. This approach could be easily improved using a weighting criteria method instead of a boolean one, although the presented methodology met the conditions to simplify the number of options to be used for future problems for the supply chain planning, control, or risk assessment. Another important extension of the present work consists in taking into account the influence of these constraints over production systems based on biomass, where final decisions can depend of particular stakeholders and to design supply chains for biofuels and related products using a mathematical programming approach.

promising facility locations and the best, for instance, economical biorefinery location. • Energy consumption to harvest biomass: In some cases, the energy to harvest and transport biomass along large distances can be higher than the produced energy from this biomass (see ref 51). In the present work, we take into account that supply chain facilities might be located in municipalities with high biomass availability to reduce transportation energy and cost. Furthermore, our work accounts for an information layer for roads and highways to ensure easy access to supply chain facilities. Besides, this methodology is not able to calculate costs or consumed energy because a full energy analysis is out of the paper scope and it is a part of a larger project with future application promoted by Mexican government. In this regard, the present work was focused on obtaining potential locations, which would be inherently more viable. • Suboptimal locations by pre-exclusion of areas: At this part, it is worth noting that for most cases the optimal supply chain topology is obtained from initial locations proposed based on previous knowledge. For example, in the studies of refs 6, 7, 14 and 3, for these cases it is possible to obtain a suboptimal or nonviable supply chain configuration because some locations are pre-excluded by authors. In the present work, it is possible to obtain suboptimal supply chain locations because some areas are pre-excluded. However, based on the proposed methodology, areas are excluded because they do not hold a set of predefined criteria for viability. In this respect, the final locations would be feasible locations to be selected by a more rigorous method such a mathematical programming approach for supply chain planning or scheduling.

CONCLUSIONS This work presents a novel approach to determine viable facilities locations for production systems based on agricultural residues under uncertain conditions. The approach has considered diverse environmental, social and technical constraints linked to geographic information. In addition, it is important to stress that the aim is to determine the best potential sites to install facilities. For this reason, in the selected sites all constraints are met. Nevertheless, some constraints may not be hold by several sites or regions, but the present strategy might be useful to determine the sites or biomass types that can be attractive. Furthermore, the associated uncertainty to the biomass availability has been taken into account though the scenarios framework. The method allows identifying in a robust way viable facility locations, which can be considered in a future supply chain design problem in order to obtain energy or other useful commodities. Besides, uncertainty analysis permitted us to select the locations with high raw material availability and low biomass production variation. This study has proved that raw material availability analysis is an important factor to consider in a biomass processing system, because reduction percentage from initial municipalities to final municipalities is greater than 70% for most of considered biomass. Also, simultaneous application of geographic restrictions (such as terrain slope, distance to highways, distance to water bodies) and uncertainty analysis for raw material have a significant contribution over selection of

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssuschemeng.7b03303. Different values for biomass availability for each considered raw material, municipality and generated scenario (XLSX) Different GIS information layers for the considered criteria. Appendix A: Results for viable facilities locations for diverse raw materials. Appendix B: Graphic representation of information layers, influence of used information layers (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*J. E. Santibañez-Aguilar. E-mail: [email protected]. *A. Flores-Tlacuahuac. E-mail: antonio.fl[email protected]. Phone: +52(1) 55 4347 2804. P


Research Article

ACS Sustainable Chemistry & Engineering ORCID

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Antonio Flores-Tlacuahuac: 0000-0001-7944-0057 Francisco J. Lozano: 0000-0002-3370-9599 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank the financial support to carry out the present ́ work to Secretariá de Energia-Conacyt trough the Binational Energy Laboratory grant and the support from the Energy and climatic change research group at Tecnológico de Monterrey.

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