Optimization of Microalgae-to-Biodiesel Production Process Using a

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Optimization of Microalgae-to-Biodiesel Production Process Using a Metaheuristic Technique Luis Germán Hernández-Pérez, Eduardo Sanchez-Tuiran, Karina Angelica Ojeda, Mahmoud M El-Halwagi, and José María Ponce-Ortega ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.9b00274 • Publication Date (Web): 05 Apr 2019 Downloaded from http://pubs.acs.org on April 7, 2019

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ACS Sustainable Chemistry & Engineering

Optimization of Microalgae-to-Biodiesel Production Process Using a Metaheuristic Technique

Luis Germán Hernández-Péreza, Eduardo Sánchez-Tuiránb, Karina A. Ojedab, Mahmoud M. El-Halwagic, José M. Ponce-Ortegaa*

aChemical

Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo,

Francisco J. Mújica S/N, Ciudad Universitaria, Morelia, Michoacan, 58060, México bChemical

Engineering Department, Universidad de Cartagena, Av. Del Consulado, Calle 30, No. 39B-192, Cartagena de Indias, Bolivar, 130015, Colombia

cChemical

Engineering Department, Texas A&M University, 100 Spence St, Jack E. Brown Building, College Station TX, 77843, USA

* Corresponding author: J.M. Ponce-Ortega E-mail: [email protected]; Tel. +52 443 3223500 ext. 1277; Fax. +52 443 3273584

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ABSTRACT This paper presents an effective computational scheme using meta-heuristic techniques for the optimization of an integrated biodiesel production process from microalgae Chlorella vulgaris. Ten decision variables were optimized including temperatures and pressures of the four process reactors and the number of stages and feed stage of the three considered distillation columns. The model is a multi-objective formulation involving economic and environmental objectives. The economic objective function is aimed at maximizing the total annual income. The objective function associated with the environmental impact is to minimize the produced greenhouse gases. Process data were obtained from the simulation software ASPEN PlusTM. Models for determining the properties for new substances missing in the database, as fatty acids present in the microalgae, were considered to improve the predictions of the raw material properties. The free software Symyx Draw® was used for the creation of all the components that were not in the ASPEN PlusTM database. The optimization tool that was used in this paper consists of a stochastic algorithm called I-MODE (Improved MultiObjective Differential Evolution). Likewise, a linking subroutine based on COM (Component Object Module) Technology and developed in Excel-Visual Basic for Applications scripts was implemented to control the ASPEN PlusTM software for various sets of decision variables. The results offer attractive options for both economic and environmental benefits.

Keywords: Multi-objective optimization, Optimal design, Biodiesel production process, Microalgae, Stochastic algorithm.


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INTRODUCTION As a result of economic development and population growth, there has been an increasing demand for natural resources and energy usage.1,2 The continued use of fossil fuels as the primary source of energy is unsustainable, this way, renewable energy sources must be incorporated in the energy portfolio, and biofuels such as biodiesel are among the most attractive options for renewable energy. Nowadays, the main form to obtain the energy demanded by the society is burning fossil fuels. However, this type of fuel has the great disadvantage that the crude, from which it is obtained, is considered a non-renewable natural resource and the excessive exploitation of oil will eventually deplete the reserves present in current deposits. This way, to satisfy the fuel demands in the last decades, renewable fuels coming from biomass have been considered as an alternative.3 These biofuels can be obtained by a renewable source, it means that it is possible to recover the mass used as raw material to produce fuel in a short period of time. Within biofuels, biodiesel represents an attractive alternative.4 Biodiesel is a liquid biofuel obtained from vegetable oils and animal fats. One of the main problems to produce biodiesel is the high consumption of water, which can represent land consumption too.5 As an alternative, microalgae have recently been considered,6 but one of the associated problems is the high cost due to the high water and energy consumptions.7 Several feedstocks and technologies for biodiesel production have been proposed. Recent review of the current technologies and feedstocks for biodiesel production have been provided by Dickinson et al.8 and Aransiola et al.9 Various pathways have been considered for biodiesel production via esterification and transesterification by Myint and El-Halwagi,10 Sánchez et al.,11 and Lee et al.3 Sustainability issues in biodiesel production have been addressed by Pokoo-Aikins et al.,12 Elms and El-Halwagi,13 and Gutiérrez-Arriaga et al.14 In these studies, computer-aided simulation was used for modeling the process. Coupling formal optimization techniques with commercial simulators is a significant challenge because of the difficulty of embedding the black-box modeling equations into mathematical programming formulations. Typically, what-if scenarios and simple parametervariation techniques are used for optimization via simulation of multiple cases. In this work, metaheuristic optimization techniques are used for optimizing process design of biodiesel production from microalgae Chlorella vulgaris by esterification (acid) and transesterification (basic) with methanol. Ten decision variables are optimized. These variables include the temperatures and pressures of the five reactors involved in the process as well as the number and feed stages of the involved distillation columns, which directly and significantly impact on the objective functions. Two objective functions are considered: economic and environmental. The economic objective function is geared towards the maximization of the total income. The environmental objective function is aimed at the minimization of the entire CO2 emissions. ASPEN PlusTM software was used to model the biodiesel process from microalgae. Detailed thermodynamic models were incorporated for determining the properties for ACS Paragon Plus Environment


new substances missing in the database, as fatty acids present in the microalgae, in order to increase the detail in the modeling of the raw material. The software Symyx DrawTM was used for creating all the components that were not available in the component list in the ASPEN Plus® database. Since the optimization problems have a large number of highly non-convex functions, the use of a stochastic algorithm is necessary. In this work, an algorithm called Improved Multi Objective Differential Evolution was used. The process simulation software is linked with MS ExcelTM which works as controller program and it is where the stochastic algorithm was developed. The results of the optimization through a series of simulations with different random values selected by the algorithm in the specified ranges offers Pareto graphics through which it is possible to offer valuable information to the decision maker. MODELLING For the optimization scheme presented in this paper, which corresponds to a multi-objective approach for the biodiesel production process from microalgae Chlorella vulgaris, the first step is to implement a model, which adequately represents the entire process behavior. A model can be used for what-if studies and process troubleshooting and it has many applications for process optimization, process control and operator training.15 In order to predict the values of the output variables for certain input variables and process design variables (including operational variables), it is necessary to use a mathematical model, which is a simplified representation of the behavior of a phenomenon in the reality. In the case of the mathematical model of a chemical process, it consists in the representation of the physical-chemical behavior of the real process.16 Models are often difficult to solve analytically, and so they are mostly solved numerically.16 Modeling and simulation are used to optimize the process operation and design,15 whereas optimization improves the performance of a process by changing the operating conditions.16 Currently, several process simulators (including Aspen PlusTM and Aspen HYSYSTM) are commercially available for simulating entire chemical processes, where standard process units and specific property databases for several chemical compounds are available.15 Also, Aspen PlusTM is a process simulation software that can be used for several thermodynamic calculations, or to retrieve and/or correlate thermodynamic and transport data. With Aspen PlusTM, one can interactively change specifications (such as flowsheet configuration, operating conditions and feed compositions) to run new cases and analyze process alternatives. It is worth noting that despite the multiple advantages of commercial simulators, such as Aspen PlusTM, the provided optimization tools are very limited, for this reason a hybrid meta-heuristic algorithm, such as the I-MODE (Improved Multi-Objective Differential Evolution), is needed for the optimization step.16 ACS Paragon Plus Environment

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Process simulation using Aspen PlusTM The simulation of the biofuel production process was developed in the Aspen Plus™ software, which is one of the most used process simulators in the chemical and process industry. The processes were simulated in steady state and they were divided by hierarchies (stages of the process) according to the selected technological route. Figure 1 shows a scheme of the simulation implemented in Aspen PlusTM. For this case study, there was considered a flowrate of 13.5 kmol/h (9,376.57 kg/h) of microalgae oil (MAO) of Chlorella vulgaris with 10% (mass base) of free fatty acid (FFA) content as raw material to produce biodiesel through esterification and transesterification processes with methanol. According to previous reports,17 it is an adequate value to establish a refinery; however, the proposed strategy is a general methodology and a different flowrate can be proposed and the results will be proportional. Information about composition and properties of MAO was obtained from the literature.18 The thermodynamic data of the components were based on the Non-Random-Two-Liquid (NRTL) method since there are some polar components in the simulation, with no-electrolytes, pressure under 10 bars, interaction parameters available and with liquid/liquid equilibrium. For the decanting stages, the Redlich-Kwong-Soave equation was considered since the components of this stage were non-polar and treated independently (non-pseudocomponents).19 The esterification and transesterification reactions were considered as conversion reactors. The FFA's present in the MAO are transformed into biodiesel and water through an esterification stage, where they react with methanol in 60:1 molar ratio (methanol: FFA's) at 60°C in the presence of sulfuric acid (5% by mass, based on to FFA's) prior to the transesterification stage. In this way, the methyl esters of fatty acids and water are obtained and separated in the decanter DEC-01. Thus, the transesterification of TG's with an alcohol occurs faster and with less technical difficulties due to the reduced presence of FFA's, which have been transformed into biodiesel and water in the transesterification stage.20 Due to the high amount of methanol present in stream 109 from DEC-01 (85.8% by mass), the process was recirculated to reduce the consumption of fresh raw materials. On the other hand, the biodiesel-rich stream is sent to a neutralization stage (RX-02) in the presence of NaOH and, subsequently, in the T-01 tower, where 99.9% of the water present is stripped. In this way, stream 116 is obtained, which has a flow of 8,355.87 kg/h and it is composed of 99.9% (mass) of TG's. Stream 116 is sent to reactor RX-03, where it reacts with 1,898.07 kg/h of methanol and 1% NaOH (mass, relative to TG's). The reaction is carried out at 60° C with a conversion of 97% (mol relative to the TG's).21 Stream 119, obtained after the transesterification, is sent to a separation stage by decantation (DEC-02), where two streams are obtained. Stream 120 (rich in glycerol) brings with ACS Paragon Plus Environment


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it 81.9% of the methanol present after transesterification. After a flashing stage, the MEOH-REC stream is recirculated to the process with a mass flow of 755.29 kg/h with a composition of 99.8% (mass) of methanol in order to reduce the consumption of fresh raw materials. The glycerol obtained after flashing is neutralized with sulfuric acid solution in the RX-04 reactor. A stream of 1,1120.19 kg/h is obtained with 78.6% (mass) of glycerol. The biodiesel-rich stream obtained after decanting (DEC-02) is sent to tower T-02, which, with 11 stages and a reflux ratio of 2.3, strips 98.0% of methanol from biodiesel. The stream thus obtained is sent to a washing step (T-03), where it is brought into contact with acidulated water (5% solution of sulfuric acid mass). Subsequently, the stream is sent to the decanter DEC-03 to separate the wash water and most of the catalyst, methanol and glycerol. This wash water is sent to subsequent stages of water treatment for future re-use. The stream 133, obtained in the decanter DEC-03, is rich in biodiesel and is sent to the tower T-04, where in an operation with 6 stages and a reflux ratio of 2.0 a biodiesel-rich stream of 8,310.445 kg/h is obtained with 96.5% (mass). Economic objective function In this paper, the maximization of the Total Annual Income (TAI) is considered as the economic objective function, which can be directly obtained from the Total Annual Production (TAP) multiplying by a Sale Price (SP) (Equation 1). The TAP is calculated by multiplying the Biodiesel Production (BP) (kg/h) times the hours in a day (24 hours) and the labored days in a year (360 days). The SP in USD of 0.72 $/kg is supposed to calculate the TAI of the BP. =

=

= =

(1) =

= 0.72

( )

()

24

(" )

360

($)

The Economic Objective Function is evaluated through simulation using the process simulator software to obtain the response variable (BP) for several sets of the decision variables. To easily view the obtained results, TAI is expressed in millions of USD per year multiplying by the TAP ($/year) times a conversion factor of 1×10-6 M$/$. The reason for considering the Economic Objective Function as the incomes due to the production output is because, for this analysis, the costs (operating and capital) are directly proportional to the immediate response variable considered (BP), and this way the results obtained would not change. However, the operating and capital costs can be calculated with parameters of production and dimensioning of the plant. Environmental objective function ACS Paragon Plus Environment

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In this paper, minimizing the Entire Annual CO2 Emissions (EAE) (Equation 2) associated with the heating (kcal/h) needed in the five reactors of the Biodiesel Production Process from microalgae Chlorella vulgaris was considered as the environmental criterion. & &=&

='

&

5

*

=1

( )

()

24

(" )

360

(2)

Where R corresponds to CO2 emissions associated to Reactor «n» (kg/h). Aspen PlusTM can calculate CO2 emissions using carbon tracking of a utility (in this paper the US-EPA-Rule-E9-5711 with a value of 2.3x10-7 kg/cal for Natural Gas was used). The CO2 energy source efficiency factor depends of the utility type, which is of 0.85 for CO2 emission associated with the fuel necessary to obtain high temperature in a specific equipment by a fired heat, and of 0.58 for CO2 emission associated with the fuel necessary to obtain electricity, which is used in this case for pumping water in cooling utilities. If a negative heat is obtained, it is because is necessary to burn by a fired heat. In the other hand, if a positive heat is obtained, it is because it is necessary to cool by pumping water in cooling utility using electricity. As can be seen, both CO2 emissions types are directly associated with the obtained heat by the simulator. To easily view the obtained results, the EAE are expressed in kt per year multiplying the EAE (kg/year) times a conversion factor of 1×10-6 kt/kg. OPTIMIZATION ALGORITHM The solution process for multi-objective optimization problems involves important difficulties, which require proper optimization strategies. In this paper, the I-MODE algorithm, developed by Sharma and Rangaiah,22 was implemented in the integrated solution approach. IMODE is a multiobjective optimization hybrid method and it works with a termination criterion using the nondominated solutions obtained as the iterative approach.22

Decision variables In this study, three different scenarios are proposed, each of which depends on the selected search variables. Scenario 1 corresponds to the operating conditions, there were selected ten decision variables as continuous (temperatures and pressures of the five considered reactors in Celsius scale and atmospheres). Scenario 2 corresponds to the design specifications, there were selected six decision variables as integer (number of stages and feed stage of the three considered distillation columns). Scenario 3 considers both the operating conditions and the design specifications as search variables. And there were required lower and upper limits for these variables, which are shown in Table 1. ACS Paragon Plus Environment


The half between the minimum (lower limit) and the maximum (upper limit) possible values were the initial values for each decision variable. No inequality constraints were incorporated in the presented optimization process. In the results section the impact of these variables on the performance of the objective functions is discussed. Parameters associated to the used algorithm The considered values for the parameters associated to implement the I-MODE algorithm consist of Population size (NP) by 10 individuals, Maximum Number of Generations (MNG) by 10, Taboo List size (TLS) by 5 individuals, Taboo Radius (TR) by 0.01, Crossover fractions (Cr) by 0.5, and Mutation fraction (F) by 0.5. GENERAL OPTIMIZATION APPROACH The Component Object Module (COM) Technology is used for implementing the optimization approach, which involves a hybrid platform linking Aspen PlusTM and Microsoft (MS) ExcelTM. 16 Using COM technology allows to add code so that the applications behave as an Object Linking and Embedding (OLE) Automation Server.23 The use of the methods of this library to interoperate with other Windows applications (such as MS ExcelTM) requires the use of a common scripting language, and Visual Basic for Applications (VBA) was chosen in this paper. During the optimization, a vector of search variables is sent to Aspen PlusTM from MS ExcelTM. The rigorous calculations for the data that identify a design of the biodiesel process are obtained via resolution of the mass and energy balances in each unit and accounting for the thermodynamic and design equations. These data are returned to MS ExcelTM from Aspen PlusTM for calculating both objective functions, and later they are evaluated and new vectors of design variables are generated (see Figure 2). RESULTS AND DISCUSSION This section presents the results for the addressed problem using the proposed methodology, was implemented on an Intel(R) Core TM i7-4700MQ CPU @ 2.4 GHz, 32 GB computer. The required computing time to obtain the Pareto optimal solutions varied from 10 to 15 minutes. Results after maximum number of generations Figures 3 to 5 present the main results obtained with the proposed strategy through a set of Pareto plots, which correspond to the obtained optimal solutions according to the implemented criteria. Scenario 1. In this scenario, the operating conditions are considered as search variables; this is, the pressure and temperature of each of the five reactors, while the design specifications of the distillation columns remain constant. The graphic of the results of Scenario 1 is shown in Figure 3. In this figure, there are highlighted three scenarios (A, B and C). Solution A has a gross TAI by 51.69


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M$/y, 9.84 kt/y, but the minimum value for EAE. Solution B (51.69 M$/y, 9.88 kt/y) presents intermediate values for both objective functions. And point C (51.69 M$/y, 10.23 kt/y) shows the minimum increase in the TAI with a considerable increase in the EAE. After the analysis of the presented figure, point B seems the best solution because it presents a good TAI with a minimum increment in the EAE. This is because there are combinations of values in the decision variables that have a considerable impact on one objective function (in this case, in EAE), while in the other, a considerable change (TAI) is not obtained. But in general, there is not considerable change in the value of the objective functions with the manipulation of these search variables. Scenario 2. In this scenario, design specifications are considered as search variables; this is, the number of stages and feed stage of the three considered distillation columns, while the operating conditions of the reactors remain constant. The graphic of the results is shown in Figure 4, where there are identified three interesting solutions (A, B and C). In point A (51.68 M$/y, 4.52 kt/y), which has a gross TAI but the minimum value for EAE. In point B (51.69 M$/y, 10.03 kt/y) there can be seen intermediate values for the considered objective functions. And point C (51.69 M$/y, 16.40 kt/y) shows the minimum increase in the TAI with a considerable increase in the EAE. After the analysis of the presented figure, point B seems the best solution because it has a better TAI with a minimum increment in the EAE. The manipulation of these decision variables significantly impacts the performance of the objective functions; however, it does not offer many alternatives to choose from the Pareto plot. Scenario 3. This scenario considers both the operating conditions and the design specifications as search variables. The Pareto solutions for this scenario are presented in Figure 5, where points A, B and C are highlighted. Point A has a gross TAI by 51.66 M$/y, 4.40 kt/y but the minimum value for EAE. In point B (51.69 M$/y, 5.35 kt/y) there can be seen intermediate values for both objective functions. And point C (51.69 M$/y, 15.97 kt/y) shows the minimum increase in the TAI with a considerable increase in the EAE. After the analysis of the presented graphic, point B seems the better solution because it has a better TAI with a minimum increment in the EAE. Optimal values for decision variables The I-MODE algorithm provides optimal values for the considered decision variables. Temperature was expressed in Celsius degrees and pressure in atmospheres. The optimal values of the considered decision variables of each scenario are shown in Table 2. The selected scenario as optimal is number 3, because this shows a considerable decrease in the EAE with respect to the actual values and it has an increase in the TAI whit respect to scenario number 2.



As can be seen in Table 2, changing the operation values of the search variables to the optimal values, represents small changes in the operating temperatures and pressures of the reactors. The operating temperature of the T-01 reactor with respect to the optimum value found represents an increase of only 1%, while the operating pressure with respect to its optimum value represents an increase of 9%. As for the B7 equipment, its operating temperature is reduced by 5% with the optimum value found and the pressure only increases by 3%. In reactor T-02, there is an increase of 8% and 6% in temperature and pressure, respectively. In reactor T-06 there is a decrease in temperature of 13% and an increase in pressure of 17%. Reactor T-07 is the one that represents the major change in the operating conditions of the selected search variables, since it would have to increase the temperature by 18% and the pressure by 29% if it is operated on the optimal values found. As far as the separation towers are concerned, two search variables were selected: the number of stages and the feeding stage. The modification of this first variable is what could mean a greater increase in the installation costs of the plant while the feeding stage does not represent a considerable modification in the installation costs. In the RadFrac B9, the number of stages would be maintained in 4 and only the feeding stage is modified, instead of being 3, it would be 4 to operate in the optimal design. In the RadFrac T-08, the number of stages would be increased by 2 to have a total of 12 and the feeding stage would be modified from 5 to 4. While in the RadFrac T-12, it would have the greatest modification in its design to increase 5 stages to get a total of 20, while feeding would remain in stage number 2. In summary, the variables that could represent a negative impact on operating costs change in a very low percentage. While the variables that modify the design of the plant do not change drastically either. The comparation between the actual and the optimal values for the operation variables are shown is Figures 6 and 7. Temperature for each reactor is shown in Figure 6, and the Pressure comparation can be seen in Figure 7. The analysis for the design variables is presented in Figures 8 and 9. The Number of Stages is shown in Figure 9 and the Feed Stage is shown in Figure 9. Optimal values for Objective Functions Table 2 also shows the value for every objective function in each scenario with the starting values for the searching variables in the simulation, the Economic Objective Function (maximum TAI) for the current values is 51.69 M$/y and for the optimal values (scenario 3) is 51.69 M$/y (the same). The environmental function (minimum EAE) with the starting values is 10.14 kt/y, whereas with the optimal values it is 5.35 kt/y. It represents a difference of 4,790 t/y, which corresponds to the 47.23% of the CO2 emissions. ACS Paragon Plus Environment

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It should be noted that the methodology proposed in this paper is general and can be applied to any process that can be implemented in Aspen Plus or other process simulator. This is because despite the complexity of the problem, the non-linearity, the level of non-convexity, the equations involved in the thermodynamic and hydraulic of the process, as well as its configuration, it is possible to solve it with metaheuristic tools such as the one used in this paper. The main advantage of hybrid evolutionary algorithms, such as I-MODE, is that they can optimize complex problems without the need to present the equations explicitly. CONCLUSIONS This paper has introduced a multi-objective optimization method for biodiesel production process from microalgae Chlorella vulgaris, with synchronized consideration of the economic and environmental aspects. The economic function is maximized, whereas the environmental function is minimized. The proposed solution strategy to solve the multi-objective problem involves a new approach to couple black-box models and computer-aided simulation with metaheuristic optimization approaches. A client-server interface was created using COM technology to call and solve the simulator software for various sets of input variables. A multi-objective optimization hybrid method (I-MODE) was used with a termination criterion using the non-dominated solutions obtained as the search progresses. The I-MODE algorithm determines the optimal Pareto graphics, where different points (sets of values) are shown and the decision-makers can choose the one that fits best for their requirements. The main advantage of this meta-heuristic optimization algorithm is that it does not require a significant manipulation of the algorithm by the user, it is enough just to specify the parameters associated with the use of the mentioned algorithm. For this reason, the mathematical formulation of the stochastic algorithm is not necessary. The methodology was applied to a case study on a biodiesel production process from microalgae Chlorella vulgaris by esterification and transesterification with methanol. The change of the values on the search variables significantly impacts the performance of the objective functions, mainly in the entire annual emissions of CO2 which were reduced by 4,790 t/y (47.23%). Furthermore, the analysis of the different scenarios from the selected search variables suggests that the design variables (including the number of stages and the feeding stage of the distillation towers) have a greater impact than the operation variables (such as the pressure and temperature of the reactors). The scientific contribution of this document is in the rigorous modeling of the case study, in the implementation of the hybrid stochastic optimization algorithm for the solution of the addressed problem, as well as in the linking strategies between programs to intercommunicate different platforms. NOMENCLATURE ACS Paragon Plus Environment


ACM

Aspen Custom Modeler

BP

Biodiesel Production

ChiTC

Chi-square Termination Criterion

COM

Component Object Module

Cr

Crossover Fraction

DE

Differential Evolution

EAE

Entire Annual CO2 Emissions

F

Mutation Fractions

FFA

Free fatty acids

GA

Genetic Algorithm

i

Target Individuals

I-MODE

Improved Multi Objective Differential Evolution

IEA

International Energy Agency

MAO

Organic matter

MNG

Minimum Numbers of Generations

MOO

Multi Objective Optimization

NP

Population Size

NRTL

Non-Random Two-Liquid Model

NSGA-II

Non-dominated Sorting Genetic Algorithm II

OLE

Object Linking and Embedding

RK

Redlich-Kwong Model

SOO

Single Objective Optimization

SP

Sale Price

SQP

Sequential Quadratic Programming

SSTC

Steady State Termination Criterion

TAI

Total Annual Income

TAP

Total Annual Production


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TL

Taboo List

TLS

Taboo List Size

TR

Taboo Radius

VBA

Visual Basic for Applications

WEC

World Energy Council

Author Information Corresponding Author *Ponce-Ortega José M. Tel. +52-443-3223500. Ext. 1277. Fax. +52-443-3273584. E-mail: [email protected] Notes The authors declare no competing financial interest.

Acknowledgements The authors acknowledge to Professors G.P. Rangaiah and S. Sharma for facilitating the I-MODE code. The authors also acknowledge the financial support obtained from the Mexican National Council for Science and Technology (CONACYT). REFERENCES [1] Pérez-Lombard, L.; Ortiz, J.; Pout, C. A review on buildings energy consumption information. Energ. Buildings. 2008, 40(3), 394-398: DOI: 10.1016/j.enbuild.2007.03.007 [2] Maclean, R.; Jagannathan, S.; Panth, B. Education and skills for inclusive growth, green jobs and the greening of economies in Asia. Technical and Vocational Education and Training: Issues, Concerns and Prospects. 2018. DOI: 10.1007/978-981-10-6559-0_1 [3] Lee, A. F.; Bennett, J. A.; Manayil, J. C.; Wilson, K. Heterogeneous catalysis for sustainable biodiesel production via esterification and transesterification. Chem. Soc. Rev. 2014, 43, 78877916. DOI:10.1039/c4cs00189c [4] Atabani, A. E.; Silitonga, A.S.; Badruddin, Anjum I.; Mahlia, T.M.I.; Masjuki, H.H.; Mekhile, S. A comprehensive review on biodiesel as an alternative energy resource and its characteristics. Renew. Sust. Energ. Rev. 2012, 16(4). DOI: 10.1016/j.rser.2012.01.003 [5] McKendry, P. Energy production from biomass (part 1): overview of biomass. Bioresour. Technol. 2002, 83(1), 37-46. DOI: 10.1016/S0960-8524(01)00118-3 [6] Huang, G. H.; Chen, F.; Wei, D.; Zhang, X. Biodiesel production by microalgal biotechnology. Appl. Energ. 2010, 87(1), 38-46. DOI: 10.1016/j.apenergy.2009.06.016 ACS Paragon Plus Environment


[7] Chen, C. Y.; Yeh, K. L.; Aisyah, R.; Lee, D. J.; Chang, J. S. Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: A critical review. Bioresour. Technol. 2011, 102(1), 71-81. DOI: 10.1016/j.biortech.2010.06.159 [8] Dickinson, S., Mientus, M.; Frey, D.; Aminihajibashi, A.; Ozturk, S.; Shaikh, F.; Sengupta, D.; El-Halwagi, M. M. A review of biodiesel production from microalgae. Clean Technol. Envir. 2017, 19(3), 637-668. DOI: 10.1007/s10098-016-1309-6 [9] Aransiola, E. F.; Ojumu, T. V.; Oyecola, O. O.; Madzimbamuto, T. F.; Ikhu-Omoregbe, D.I.O. A review of current technology for biodiesel production: State of the art. Biomass Bioenergy. 2014, 61, 276-297. DOI: 10.1016/j.biombioe.2013.11.014 [10] Myint, L. L.; El-Halwagi, M. M. Process analysis and optimization of biodiesel production from soybean oil. Clean Technol. Environ. Policy. 2009, 11(3), 263-276. DOI: 10.1007/s10098-008-0156-5 [11] Sánchez, E.; Ojeda, K.; El-Halwagi, M.; Kafarov, V. Biodiesel from microalgae oil production in two sequential esterification/transesterification reactors: Pinch analysis of heat integration. Chem. Eng. J. 2011, 176–177, 211–216. DOI: 10.1016/j.cej.2011.07.001 [12] Pokoo-Aikins, G.; Nadim, A.; Mahalec, V.; El-Halwagi, M. M. Design and analysis of biodiesel production from algae grown through carbon sequestration. Clean Technol. Environ. Policy. 2010, 12(3), 239-254. DOI: 10.1007/s10098-009-0215-6 [13] Elms, R. D.; El-Halwagi, M. M. The effect of greenhouse gas policy on the design and scheduling of biodiesel plants with multiple feedstocks. Clean Technol. Environ. Policy. 2010. 12(5), 547-560. DOI: 10.1007/s10098-009-0260-1 [14] Gutiérrez-Arriaga, C.; Serna-González, M.; Ponce-Ortega, J. M.; El-Halwagi, M. M. Sustainable integration of algal biodiesel production with steam electric-power plants for greenhouse gas mitigation. ACS Sustain. Chem. Eng. 2014, 2(6), 1388-1403. DOI: 10.1021/sc400436a [15] Sharma, S.; Rangaiah, G. P. Mathematical modeling, simulation and optimization for process design. In: G. P. Rangaiah, ed. Chemical Process Retrofitting and Revamping: Techniques and Applications. John Wiley & Sons, Ltd, Singapore, 2016. [16] Ponce-Ortega, J. M.; Hernández-Pérez, L. G.; Optimization of Process Flowsheets through Metaheuristic Techniques. Springer. Switzerland, 2019. [17] Sánchez E.; Ojeda, K.; El-Halwagi, M.; Kafarov, V. Biodiesel from microalgae oil production in two sequential esterification/transesterification reactors: Pinch analysis of heat integration. Chem. Eng. J. 2011, 176-177, 211-216. DOI: 10.1016/j.cej.2011.07.001 [18] Petkov, G.; Garcia, G. Which are fatty acids of the green alga Chlorella? Biochem. Syst. Ecol. 2007, 35(5), 281-285. ACS Paragon Plus Environment

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[19] Carlson, E. Don’t gamble with physical properties for simulation. Chem. Eng. Prog. 1996, 10, 35-46. [20] Marchetti, J. M.; Miguel, V. U.; Errazu, A. F. Techno-economic study of different alternatives for biodiesel production. Fuel Process. Technol. 2008, 89(8) DOI: 10.1016/j.fuproc.2008.01.007 [21] Rashid, U.; Anwar, F.; Moser, B. R.; Ashraf, S. Production of sunflower oil methyl esters by optimized alkali-catalyzed methanolysis. Biomass Bioenergy. 2008, 32(12), 1202-1205. DOI: 10.1016/j.biombioe.2008.03.001 [22] Sharma, S.; Rangaiah, G. P. An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput. Chem. Eng. 2013, 56, 155173. [23] Woinaroschy, A. Simulation and Optimization of Citric Acid Production with SuperPro Designer using a Client-Server Interface. Rev. Chim. 2009, 60(9), 979-983.



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Tables Table 1. Limits for decision variables. Search Variables Equip

Type

Variable

Reactor T-01

Continuous

Reactor B7

Continuous

Reactor T-02

Continuous

Reactor T-06

Continuous

Reactor T-07

Continuous

RadFrac B9

Integer

RadFrac T-08

Integer

RadFrac T-12

Integer

Unit

Actual

Boundaries

Value

Lower Upper

Temperature

°C

60.00

50.00

70.00

Pressure

atm

1.00

1.00

1.50

Temperature

°C

60.00

50.00

70.00

Pressure

atm

1.00

1.00

1.50

Temperature

°C

60.00

50.00

70.00

Pressure

atm

1.00

1.00

1.50

Temperature

°C

25.00

20.00

30.00

Pressure

atm

1.00

1.00

2.00

Temperature

°C

25.00

20.00

30.00

Pressure

atm

1.00

1.00

2.00

Number Stage

no.

4

4

8

Feed Stage

no.

3

3

4

Number Stage

no.

10

8

16

Feed Stage

no.

5

2

8

Number Stage

no.

15

10

20

Feed Stage

no.

2

2

10


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Table 2. Optimal values for decision variables. Performance Analyze

Equip

Type

Variable

Unit

Actual Value

Optimal Value Scenario

Scenario

Scenario

1

2

3

Continuous Temperature

°C

60.00

58.50

60.35

Continuous Pressure

atm

1.00

1.15

1.09


°C

60.00

60.28

56.83

Continuous Pressure

atm

1.00

1.35

1.03


°C

60.00

68.52

64.68

Continuous Pressure

atm

1.00

1.31

1.06


°C

25.00

21.88

21.63

Continuous Pressure

atm

1.00

1.30

1.17


°C

25.00

25.95

29.51

Continuous Pressure

atm

1.00

1.21

1.29

Integer

Number Stage no.

4

4

4

Integer

Feed Stage

no.

3

4

4

RadFrac T-

Integer

Number Stage no.

10

12

12

08

Integer

Feed Stage

no.

5

5

4

RadFrac T-

Integer

Number Stage no.

15

17

20

12

Integer

Feed Stage

2

2

2

Reactor T01 Reactor B7 Reactor T02 Reactor T06 Reactor T07 RadFrac B9

no. TAI =

51.70

51.70

51.68

51.69

10.03

9.88

4.52

5.35

EAE =



Cation for Figures:

Figure 1. Flowsheet for the Biodiesel Production Process from microalgae Chlorella vulgaris. Figure 2. Implemented links for the proposed approach. Figure 3. Graphic of the results for Scenario 1. Figure 4. Graphic of the results for Scenario 2. Figure 5. Graphic of the results for Scenario 3. Figure 6. Graphic of the temperature comparation. Figure 7. Graphic of the pressure comparation. Figure 8. Graphic of the number stage comparation. Figure 9. Graphic of the feed stage comparation.


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Figure 1. Flowsheet for the Biodiesel Production Process from microalgae Chlorella vulgaris.




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Results after MNG 10.3 10.25

CO2 emissions (kt)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


C

10.2 10.15 10.1 10.05 10 9.95 9.9 9.85 9.8 51.69745

B

A 51.6975

51.69755

51.6976

Total Income (M$) Figure 3. Graphic of the results for Scenario 1.


51.69765

51.6977


Results after MNG 18

C

16

CO2 emissions (kt)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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14 12

B

10 8 6

A

4 2 0 51.68 51.682 51.684 51.686 51.688 51.69 51.692 51.694 51.696 51.698 51.7



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Results after MNG 18

C

16

CO2 emissions (kt)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


14 12 10 8 6 4

B

A

2 0 51.66

51.665

51.67

51.675

51.68

51.685

51.69

51.695



51.7

51.705


Value Comparation 70

Temperature (ºC)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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60 50 40 30

Actual

20

Optimal

10 0 Reactor T-01

Reactor B7

Reactor T-02

Reactor T-06

Equip

Figure 6. Graphic of the temperature comparation.


Reactor T-07

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Value Comparation 1.40 1.20

Pressure (atm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1.00 0.80 0.60

Actual

0.40

Optimal

0.20 0.00 Reactor T-01

Reactor B7

Reactor T-02

Reactor T-06

Equip

Figure 7. Graphic of the pressure comparation.


Reactor T-07


Value Comparation 25

Number Stage

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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20 15 Actual

10

Optimal 5 0 RadFrac B9

RadFrac T-08

RadFrac T-12

Equip

Figure 8. Graphic of the number stage comparation.


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Value Comparation 6 5

Feed Stage

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


4 3 Actual 2

Optimal

1 0 RadFrac B9

RadFrac T-08

RadFrac T-12

Equip

Figure 9. Graphic of the feed stage comparation.




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Optimization of Microalgae-to-Biodiesel Production Process Using a

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