Supervised life-cycle assessment using automated process inventory

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Supervised life-cycle assessment using automated process inventory based on process recipes Edrisi Muñoz, Elisabet Capon-Garcia, and Luis Puigjaner ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/acssuschemeng.7b04154 • Publication Date (Web): 23 Jul 2018 Downloaded from http://pubs.acs.org on July 24, 2018

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ACS Sustainable Chemistry & Engineering

Supervised life-cycle assessment using automated process inventory based on process recipes Edrisi Muñoz a, Elisabet Capón-Garcia b, Luis Puigjaner*c a

Centro de Investigación en Matemáticas A.C., Jalisco S/N, Mineral y Valenciana 36240, Guanajuato, México b

c

ABB Switzerland Ltd., Segelhofstrasse 1K, 5405 Baden-Dättwil, Switzerland

Centre d’Enginyeria de Processos i Medi Ambient, UPC, EEBE - c. Eduard Maristany 10-14, Ed. I-5, 08019 BARCELONA Universitat Politecnica de Catalunya. *

[email protected]

ABSTRACT: Effective integration of environmental issues and process decisions is crucial for enhanced enterprise operation from an environmental perspective. In this sense, environmental assessment involves the transaction of large amount of data and information. Hence, tools for improving information sharing and communication have proved to be highly promising to support the integration of environmental assessment within industrial decision-making. This work aims to automate the creation of the life cycle inventory (LCI) of production processes and products based on their recipe information. A framework based on a knowledge model of the process and environmental domains relying on ISA-S88 standard for recipe representation has

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been developed. As a result, the environmental assessment can be directly derived, and so the environmental indicators are available to the decision maker, thus reducing the data collection and processing efforts. The framework is applied to a case study, namely an acrylic fiber production process, which comprises 14 production steps and over 11 different production resources. The framework stands for an enterprise decision-making support tool, which recognizes the different environmental elements associated with production recipes, facilitating environmental assessment of production processes. KEYWORDS: Life cycle inventory assessment, Knowledge model, Process integration, Data mining, Environmental performance indicators

INTRODUCTION Enterprises comprise highly complex systems, which need to coordinate. Hence, addressing environmental and process issues while satisfying customer and regulatory requirements becomes a difficult task. Several authors have highlighted the importance of considering life cycle assessment of production processes along the whole supply chain[1,2,3]. In addition, waste minimization, material recovery and utilities consumption in process operations should be also considered. In the literature, several methodologies are used to assess the environmental impact of industrial activities, such as, i) the Minimum Environmental Impact[4]; ii) the Waste Reduction Algorithm[5] which uses the pollution balance concept and the environmental fate and risk assessment tool[6]; and iii) the critical surface-time 95 assessment methodology[7]. The common factor among the aforementioned methodologies is life cycle assessment (LCA). Within LCA, the overall life cycle of processes or products is analyzed, taking into account upstream and downstream flows from cradle to grave. LCA allows to quantify the potential environmental


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impacts, providing a wide set of metrics capable of contributing to an overall picture of the system. A wide range of LCA available software packages, such as, PEMS and SimaPro include reliable databases on materials, energy, transport and waste management issues[8,9]. LCA comprises four main phases, namely (i) the goal and scope definition, (ii) the inventory analysis, (iii) life cycle impact assessment, and (iv) interpretation. The goal and scope definition comprises the statement of the problem, the target audience and the intended application. Specifically, the temporal and geographical boundaries are defined, and the elements to be studied are established. The inventory analysis defines the product system, including the system boundaries and the flow diagrams. It results in an inventory table quantifying the inputs and outputs to the environment. The life cycle impact assessment consists of processing the results from the inventory analysis in terms of environmental impacts using a list of impact categories. In this phase, the different impacts can be grouped and weighted in category indicators. The final step consists of the interpretation of the results, which is highly user dependent. This work focuses on the automated creation of environmental process inventory and assessment of production processes based on their production recipe. Therefore, a decision support framework has been developed considering the traditional formalization of process workflows in recipes. Many efforts have already been devoted to develop improved analytical models, as well as, models integrated with transactional systems in order to support decisionmaking. However, the integration of recipe models and environmental information has not been tackled yet. A key aspect is the need of data and information quality that must be available and continuously updated regarding plant operations, market conditions and environmental issues. Hence, it is of utmost importance to develop automated data management systems. For many


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years, companies have developed information systems to support end users to exploit data and models, with the final objective of improving the decision-making task. These decisions are related to manufacturing characteristics, which are essential for the viability and competitiveness of the enterprise[10]. Thus, infrastructures that continuously and coherently support fast and reliable transactional data from activities related to production process are crucial element in these information systems. Recent activity focuses in the fields of online analytical processing (OLAP), data mining and Web-based DSS, as well as collaborative support systems and optimization-based decision support[11]. It is quite common that production process systems work with large databases or databases clusters. Hence, an enormous amount of information can be created, stored and shared, resulting in a task hard to manage. Furthermore, interoperability among those systems is critical for data and information integration, where systems can differ on codified language and conceptualization. The use of multiple models to represent detailed and abstract knowledge of chemical processes has been taken into account recently. In particular, knowledge models enable identifying and matching processes with their functions, objectives and relations within enterprise structure. Moreover, those models allow the generation of different process views regarding different levels of process abstraction, such as strategic, tactical and operational perspectives. Ontologies constitute a means of specifying the structure of a domain of knowledge in a systematic way that can be read by a computer (formal specification) and presented in a human readable form (informal specification). As a result, ontologies are emerging as a key solution to knowledge sharing in a cooperative business environment[12]. Finally, due to ontologies potential for expressing knowledge in a clear semantically manner, they are expected to play an important role in forthcoming information-management solutions to improve the


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information search process[13, 14]. Ontological models formalize knowledge representation, which comprise concepts (represented by terms), properties and relations among those concepts; knowledge domain characterization; and formal specifications of the intended meaning of such terms[15]. At the enterprise level, several ontological approaches are proposed to reach information system interoperability[16]. In this sense, a review on supply chain ontological models by Grubic and Fan[17] warned about the fact that methodological approaches are too remote and do not account for all material and information flows. Therefore, alternatives have been proposed to integrate scheduling and control[18] as well as the whole supply chain[19] with information systems relying on ontological models. Even more, these systems have been designed to encompass environmental information regarding industrial processes[20]. At LCA domain some developments have been presented, such as, a knowledge representation of environmental issues[21]. That framework modeled expert knowledge for integrated environmental assessment of technologies and processes associated with industrial ecology. Besides, the authors present a prototype software consisting of an upper ontology, logical inference reasoners and a multi-agent system, which deals with industrial ecology with a focus on human activities and industrial impacts on the environment. An LCA ontology proposed by [22]

included a domain based on ISO 14040[23]. The resulting model was an ontology containing

various phases of the LCA. Likewise, ontologies have been developed for supporting eco design of processes[24]. Specifically, ontologies have been used for capturing the knowledge of every single case as expert systems do, and they support reasoning and query for certain information. More recently, Takhom et al.[25] describe a collaborative approach to ontology development for data qualification for life cycle assessment by taking into consideration the Life Cycle Inventory (LCI) and Data Quality Indicator (DQI). The ontology has been integrated with rule-based


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knowledge, to provide user defined policies for LCI based on DQI. Overall, the use of ontological models for acquiring practical information regarding the application of LCA has been relevant in the literature; however, the integration and automation of data collection of industrial processes for their environmental assessment has not been addressed yet. This paper aims to automate the creation of the life cycle inventory (LCI) of production processes and products based on their recipe model. As a result, the life cycle assessment (LCA) can be directly derived, and so the environmental indicators will be available to the decision maker, thus reducing the data collection and processing efforts. Therefore, this work develops a decision support framework relying on recipe information and the information workflow, comprising two main algorithms for life cycle inventory and assessment creation. This approach is based on a semantic model framework representing an integrated domain framework that considers the environmental system representation within the various enterprise decision levels. In addition, the semantic model facilitates integration among transactional systems and analytical models resulting in a sustainable solution for the design and operation of process models. Thus, the environmental assessment of production processes can be automatically obtained to support decision-making. A case study on acrylic fibers production illustrates the usability of the approach for environmental decision-making. Specifically, this case study presents how recipe models are used to create the life cycle inventory of the processes. METHODOLOGY Recipe management Recipe management systems aim to formalize and coordinate production process requirements in order to supporting decision-making. Decision support systems (DDSs) are defined as computer-aided systems at the company management level that combine data and sophisticated


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analytic models to support decision-making[26]. Usually, DSSs design is comprised of components for sophisticated database management capabilities with access to internal and external data, as well as punctual information; modeling functions; simple user interface for queries, reporting, and graphing functions; and access to analytical tools. The role of the recipe management system consists of creating, storing and maintaining the different types of recipes, including the contained information. In this work, the recipe concept relies on the definition provided by ANSI/ISA 88 standard [2730]

, namely a recipe is an entity containing “the necessary set of information that uniquely defines

the production requirements for a specific product”. The standard also provides a formalized terminology and a consistent set of concepts and models for batch manufacturing plants and batch control. Likewise, the standard presents four types of recipes, namely general, site, master, and control recipes, which apply at different operational levels and contain several categories of information, as described in Table S1, in the supporting information. On the one hand, each recipe comprises information about the manufacturing of a product in varying degrees of specificity and detail. From general to specific, firstly, the general recipe identifies raw materials, their relative quantities, and required processing. General recipe may be used as a basis for enterprise-wide planning and investment decisions. Next, site recipe meets the conditions found at a particular manufacturing location and provides the level of detail necessary for site-level, long term production scheduling. Thirdly, master recipe has to be sufficiently adapted to the properties of the process cell equipment to ensure the correct processing of the batch. The master recipe may contain product-specific information required for detailed scheduling, such as process input information or equipment requirements and without it no control recipes can be created and, therefore, no batches can be produced. Finally, the control


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recipe starts as a copy of a specific version of a master recipe and is then modified as necessary with scheduling and operational information to be specific to a single batch. It also contains product-specific process information necessary to manufacture a particular batch of product. It provides the level of detail necessary to initiate and monitor equipment procedural entities in a process cell. On the other hand, the scope of general/site recipes and master/control recipes largely differs. The general and site recipes describe the technique, that is, how to make a specific process in a general manner. In contrast, master and control recipes describe the task, specifying how to do that specific process taking into account actual resources. In practice, the procedural elements of the master/control recipes derive from the general/site recipes. In this sense, the recipe management system is responsible for generating, updating, sharing and maintaining information within and among the different recipes, as summarized in Figure S1, in the supporting information. The procedural steps of the general and site recipes, are the referred to as "General Recipe Procedural Elements" and consist of process stages, process operations and process actions. These procedural elements are necessary for (i) the creation of the general and site recipes, and (ii) the definition of the "Master Recipe Procedural Elements", namely recipe unit procedures, recipe operations and recipe phases (Table S2). Precisely, one of the most critical roles of the recipe management system consists of mapping of the process stages, process operations and process actions defined in the site recipes into unit procedures, operations and phases contained in the master recipe. All the aforementioned procedural elements, as well as the recipes themselves contain the socalled formula, which gathers the data referring to the specific element/recipe requirements in the so-called input, output and process parameters. Therefore, this work considers this hierarchical


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definition of recipes in terms of their procedural elements and the process definition according to the formula in order to analyze their resource requirements and generate the corresponding life cycle inventory as presented in the following sections. Ontological framework This work is based on the application of an ontology following ANSI/ISA-88 and ANSI/ISA95 standards[27-31], which allows the modeling of any process system, namely Enterprise Ontology Project (EOP). The Enterprise Ontology Project, presented in Muñoz et al.

[18]

, is a

semantic model approach for representing an integrated enterprise framework that considers the environmental system representation within the various SC hierarchical decision levels. The major features of this semantic model are: i) the capability of modeling any enterprise reality, which includes its environmental implications; and ii) the linking between transactional and analytical systems within the enterprise. The model is based on the understanding and management of operational concepts (recipes, physical models, procedures, functions and processes) and activities (operational, tactical and strategic functions) provided by ANSI/ISA-88 and ANSI/ISA-95 process standards and complemented by other handbooks and reviews 33]

[16, 32,

. Figures S2 and S3 show main classes within the enterprise ontology project and resource

structure, respectively. Since the recipe model is defined from an operational perspective, material resources can be further classified as raw material, final product, intermediate, byproduct and residue. Residues cannot be further recuperated for the process, and they are discharged to the environment; therefore, residues are equivalent to environmental releases. Besides the ontology metrics of EOP are described in Table S3, in the supporting information.


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The different recipe types and their procedural elements are represented as classes in the ontology. Figure S4 contains the relevant object of these classes, which are extracted and analyzed for the creation of the life-cycle inventory as presented in the following section. As depicted in Figure S4, the common element of all recipes and recipe procedural elements is the formula. Indeed, the formula contains information related to input, output and process parameters, which are a subclass of “Parameter”. However, depending on the scope of the recipe or recipe procedural element, the value and content of the fields in these parameters may differ. The model for the Parameter class is illustrated in Table S4. The crucial object property relating the parameter to an actual resource of the process is the so-called hasParameterSource, whose range is the class “Resource”. This modelling strategy for process resources allows to distinguish between resources themselves (as “Resource”), and how these resources are used in the process (as “Parameter” linked to a specific “Resource”). Likewise, the class “Parameter” and its subclasses have an ID, and they can be either considered constant or their value can be computed derived from a formula. In the former case, the property “value” holds the numerical value while in the latter, the property “hasEquationAsReferenceValue” refers to an ontological description of the equation that describes the value of the parameter. Finally, the “engineering_units” property contains the unit of measure for the referred parameter. The different subclasses of the resource class have been illustrated previously in Figure S3. The object and data properties of the resource class are presented in Table S5. However, the most important property required for creating the life cycle inventory and assessment is the “hasEnvironmentalPerformanceIndex”. The other properties can be used to develop an economic assessment of the recipe and perform sensitivity analyses.


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Precisely, the class “EnvironmentalPerformanceIndex” stands for the connection within the EOP ontology for carrying out the environmental assessment of the process (properties in Table S6). Therefore, EOP also involves environmental aspects for the decision-making process based on assessing and evaluating the environmental impact of the activities using the information about consumed and produced resources. In order to manage transactional system data regarding LCA, the inclusion of environmental issues within a semantic representation of the enterprise structure, improves the transaction of available data and generates information quality in order to improve decision-making. The environmental domain within the enterprise domain can be traced back to the environmental management system, which may be assessed by a set of environmental metrics. Figure S5 shows main features of EOP comprising LCA domain. Besides, Figure S6 shows representation of the environmental domain (extract of the Unified Modeling Language (UML) representation). This extract presents some of the main classes and properties, used to assess the environmental performance of the processes and products, related to end-point and mid-point categories. UML is used to helps specify, visualize, and document models of software systems (structure and design), in a way that meets all of these requirements in a software process development. As presented in the following two sections, EOP is used to implement the LCA for a given process or product requiring data associated with process environmental interventions. These interventions are related to issues such as raw material consumption, uncontrolled emissions and waste generation. This set of data is organized in a life cycle inventory (LCI) that is the basis for the environmental impact calculation, as specified in the ISO 1404X series. The LCI calculation can be related to the energy and mass balance flows, which are already contained in the recipe


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structure of the process. Next, the coordination of the LCI with the information environmental impact and the different impact categories will result in the assessment of the whole process based on selected indicators, and the whole recipe structure allows locating the specific source/sink points on the cause-effect chain. Algorithm for inventory creation The main pre-requisite for creating the life-cycle inventory of a given production process consists of modeling its recipe using the EOP ontology. Such instance of the problem automatically complies with the ANSI/ISA 88 standards, and has a defined terminology, since it shares the classes, properties and rules of the EOP model, and contains the individuals of the process recipe. Therefore, given a process recipe modeled in EOP, the algorithm for automating the creation of life cycle inventory (Algorithm 1) can be applied. The whole framework has been programmed in Jython, since this programming language reads the OWL API for and has the potential to access Python libraries for natural language processing[19]. The information flow for the process is depicted in Figure 1. The user creates the instance of the process recipe within the EOP model. Next, the framework parses .owl file and requests the required information about the environmental assessment to the user as explained below. After processing the provided information, the LCA information is displayed to the user.


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Algorithm 1: Automated resource inventory creation based on process recipe. Data:

Instance of process recipe in Enterprise Ontology Project

Result:

Table with resource inventory for LCI

begin 1. define system boundaries and scope 2. extract formula information 3. riTable ← create resource inventory table 4. elements ← extract subsystem elements 5. if elements eriTable ← emty for i in elements do repeat from 1. to 5. for system boundaries defined by i eriTable ← compile information from LCITable(i) 6. LCITable ← provide reconciled riTable and eriTable

Figure 1. Proposed information flow for conducting the environmental assessment based on process recipe: (a) the user creates an instance of the assessed process recipe within the ontological EOP model; (b) the user provides information for defining the LCA steps according


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to the created procedure (algorithms 1 and 2); (c) the user receives process life-cycle inventory and environmental performance metrics. The first step consists of establishing the system boundaries from the recipe structure, as well as the definition of the functional unit for the product or process. The system boundaries are defined as a function of the physical model or the process model, which delimits the studied system. Therefore, when the user requests a new LCI, the framework requests the definition of the system boundaries using a tree search of the existing elements in model structure comprising classes and instances within the recipe and recipe procedural elements (Figure S7). After delimiting the boundaries of the system according to the recipe definition, the framework extracts the recipe information from the problem instance. On the one hand, the formula associated to the selected recipe element is analyzed, and the different input/output/process parameters contained in the formula are mapped and consolidated with the information regarding their parameter sources, thus resulting in a resource inventory table (riTable). Such information is retrieved based on the structure of the ontological model presented in the previous section. On the other hand, the recipe procedural elements contained in the system boundaries are tracked according to the ontological model structure defined in Figure 4. As a result, the recipe subsystems (elements) are identified and are available for further analysis and consistency checking regarding resource consumption and generation (Figure 2).


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(a) (b) (c)

(d)

Figure 2. Screen displaying the riTable and the recipe procedural elements contained in the system boundaries (a: this pane presents the recipe element of the LCI table; b: this pane presents the formula that defines the LCI table; c: this pane includes the recipe elements included in the system boundaries and the button “Compile” allows for compiling the information of these elements; d: this pane presents the LCI table entries). Next, for the subsystem boundary defined by each element i in the list of subsystems, the procedure defined by steps 2 to 5 is repeated, thus keeping track of the resources and the corresponding quantities consumed or produced, in order to store and group them in a preliminary inventory (eriTable). Those entries that are double counted as well as those parameters from different recipe elements that balance are also included, but highlighted for informing the decision-maker. Next, the decision maker can discard those rows in the Inventory Table that are not desired for the life cycle inventory table. The result of this phase consists of a reconciled inventory table (LCITable), which is automatically created from the defined process recipe and recipe procedural elements, whose final approval depends on the decision-maker knowledge and experience. Algorithm for impact category assessment


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The development of the impact assessment completely depends on the life cycle inventory obtained in the previous step. Algorithm 2 summarizes the steps for the implementation of the environmental assessment in this work. Algorithm 2: Automated environmental assessment creation based on inventory table. Data:

Table with resource inventory for LCI for the desired process Instance of process recipe in Enterprise Ontology Project

Result:

Table with environmental performance metrics.

begin 1. metrics ←define environmental impact categories 2. for i in metrics do identify and request missing values eaiValue ← compile information from EOP instance and LCITable 3. eaTable ← create environmental assessment table Firstly, the user needs to select the environmental impact categories within the scope of the impact category assessment. Therefore, the resources in the LCI table are examined, and the impact categories associated with them by means of the environmental performance indices (Tables S5 and S6) are gathered. As a result, a complete list of available impact categories is presented to the user (Figure S8). The next step consists of automating the computation of the numerical value corresponding to the different impact categories selected by the user. For each category i, the framework identifies which resources from the LCITable have not been assigned an environmental performance index whose impact category corresponds to i. Next, the user is prompted to fill in the environmental performance index for the identified resources. After completion, the framework computes the


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individual impact category value based on the levels presented in the LCITable and the environmental performance indices of the resources (eaiValue). Finally, the framework displays impact category Table (eaTable) and provides the decisionmaker the option to rank the resources according to the relevance in the impact category indicator (Figure 3).

Figure 3. Screen displaying the eaTable and the recipe procedural elements contained in the system boundaries for a given environmental impact category (a: this pane presents the recipe element of the LCA table; b: this pane presents the name of the selected environmental performance indicator; c: this pane shows the total value of the performance indicator and its units; d: this pane includes the recipe elements included in the system boundaries and the button “Compile” allows for compiling the information within these elements; e: this pane presents the LCA table entries; f: this pane allows to select a different indicator).


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CASE STUDY Production of acrylic fibers polymerization process This case study considers the production plant presented in the work by Capon-Garcia et al

[34]

,

where acrylic fibers are produced along 14 stages in a batch production plant (Figure S9), structured in 8 recipe unit procedures and 6 recipe operations. The production process is defined in a master recipe involving 27 different resources, which are basically material and energetic resources. Two alternative production processes are assessed in this work, namely acrylic fiber A uses acetone as solvent in the polymerization and acrylic fiber B uses benzene. The formulas of the master recipes are provided in Tables S7 and S8 correspondingly, whereas the formulas for each individual step are provided in Tables S9 and S10 in the supporting information. The instantiation of the two master recipes includes the modelling of the aforementioned recipe unit procedures and recipe operations, as well as their corresponding formula and input, output and process parameters, along with the environmental performance metrics parameters. Overall, the instantiation results in 934 individuals within the ontology. It is noteworthy to mention that, although unit procedures are further decomposed into operations, and operations are further decomposed in phases, this case study focuses on the level of accuracy required to compute the life cycle assessment, which consists of the recipe unit procedures and the linking recipe operations. The case study aims to illustrate the steps to perform the environmental assessment of the production process for 1 ton of the acrylic fibers A and B production process from cradle to gate. The different assumptions regarding the environmental assessment are provided in the supplementary material.


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This case study illustrates the different steps of the procedure described in the methodology. Figures 4 and 5 present the LCI table for the master recipe of acrylic fibers A and B and display the 5 different input parameters, 5 output parameters and the 4 process parameters, as well as their parameter sources, resource names, values and engineering units.

F Figure 4. Screenshot of the framework displaying the riTable for the master recipe of acrylic fiber A production process.

Figure 5. Screenshot of the framework displaying the riTable for the master recipe of acrylic fiber B production process.


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In this sense, when compiling the information of the different recipe unit procedures and recipe operations, their LCI Tables are automatically processed (Figure S10). As described in the methodology section, the next step consists of computing the life cycle impact assessment based on each life cycle inventory table and the individual resource impact (emissions, residues, electricity, water, steam and raw material). As a result, an automated LCIA relying on the decision-makers criteria for impact assessment is obtained. In this case, we have selected two intermediate environmental performance indicators, namely carcinogens and global warming. Figures 6 and 7 present the automatically generated life cycle assessment tables for the master recipes of acrylic fibers A and B, respectively. In this case study, the production of acrylic fiber A has a lower impact in terms of carcinogens, compared to fiber B; and instead, it has a higher impact in terms of global warming. Further metrics can be requested by the decision


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maker in order to ultimately reach a decision about which production process should be selected.

(a)

(b)

Figure 6. Life cycle assessment table (eaTable) for the master recipe of acrylic fiber A production process for carcinogens (a) and global warming (b).


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Figure 7. Life cycle assessment table (eaTable) for the master recipe of acrylic fiber B production process for carcinogens (a) and global warming (b). Therefore, this case study demonstrates the usefulness of the proposed framework in order to extract environmentally relevant information from the recipe structure of the production process, thus supporting the decision making process. Indeed, the previous impacts can be used as input parameters for optimization frameworks. Likewise, this methodology can be applied to monitor the production process from an environmental perspective in order to assess critical points of the production process. Furthermore, this framework allows to create the assessment for the control


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recipe, which could be functionally coupled to the master recipe in order to identify batch-tobatch production deviations and bottlenecks, and assess their actual environmental impact.

CONCLUSIONS

Decision-making becomes highly challenging in the alignment of environmental compliant decisions to succeed in business goals. Specifically, environmental establishment is closely related to several decisions among the enterprise structure, requiring the transaction of large amount of data and information. Hence, effective integration of environmental issues and process decisions may play a crucial role for the enhanced enterprise operation from an environmental perspective. This work presents a knowledge-based platform for improving information sharing and communication for supporting this integration task. This work presents an algorithm for automating the creation of the life cycle inventory (LCI) of production processes and products based on their recipe model. As a result, the life cycle assessment (LCA) can be directly derived, and so the environmental indicators are available to the decision maker, thus reducing the data collection and processing efforts. This approach is supported by a knowledge model of process and environmental domain, which are used as the technology for information and knowledge sharing for the environmental assessment of the enterprise. The results regarding the process environmental indicators provided by this framework could be further coupled with other existing ontologies on LCA for deriving rules based on their environmental evaluation.

SUPPORTING INFORMATION Additional Figures (S1 to S10) Additional Tables (S1 to S11)


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Definitions Data for the multiproduct batch plant producing acrylic fibers

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Synopsis This work automates the creation of life cycle inventory and assessment of production processes/products based on their recipe model.


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Supervised life-cycle assessment using automated process inventory

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