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Decision Support for Enhancement of Manufacturing Sustainability: A Hierarchical Control Approach Majid Moradi Aliabadi, and Yinlun Huang ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b04090 • Publication Date (Web): 05 Mar 2018 Downloaded from http://pubs.acs.org on March 9, 2018

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ACS Sustainable Chemistry & Engineering

Decision Support for Enhancement of Manufacturing Sustainability: A Hierarchical Control Approach§ Majid Moradi-Aliabadi and Yinlun Huang* Department of Chemical Engineering and Materials Science Wayne State University, Detroit, MI 48202 *[email protected]

Abstract From the sustainability science point of view, improvement of manufacturing sustainability involves a series of system (performance) state transitions in a sustainable development (SD) space. The transitions occur based on the system information obtained through stage-wised sustainability assessment and the actions on the system taken based on the derived stage-wised decisions. From the standpoint of control engineering, this is a multi-objective system control problem. In this paper, we introduce a general decision-support framework for deriving strategies to achieve short-to-long-term sustainability goals. In this framework, a two-layered hierarchical control scheme is introduced to implement strategic and tactical control for dynamic sustainability control. A comprehensive case study on biodiesel manufacturing is illustrated to demonstrate methodological efficacy.

Keywords: Sustainability control, decision making, Pareto optimality, sustainable manufacturing

____________________________________________________________________________ § *

For publication in ACS Sustainable Chemistry & Engineering (special issue on Systems Analysis, Design, and Optimization for Sustainability). All correspondence should be addressed to Prof. Yinlun Huang (Phone: 313-577-3771; E-mail: [email protected]).

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Introduction

Sustainable manufacturing is about “the creation of manufactured products through economically-sound processes that minimize negative environmental impacts while conserving energy and natural resources”; it “also enhances employee, community, and product safety”. 1 This definition contains two important aspects: (i) triple-bottom-line-balanced manufacturing, and (ii) continuous efforts for progressive improvement. While the former reflects the nature of sustainability that is a complex multi-objective optimization problem, the latter is a nonconventional dynamic control problem. In short, sustainable manufacturing is a multi-objective dynamic control problem. This is a very challenging area of sustainability research, which could be resorted to system control science in the development of holistic and effective methods and tools for advanced sustainable manufacturing research and practice. Over the past decade, various methodologies and approaches have been proposed to study sustainable manufacturing problems of different scopes and with system boundaries, most of which are using Multi Criteria Decision Making (MCDM) techniques. Note that MCDM can be classified into two main groups: the Multiple Attribute Decision Making (MADM) and Multiple Objective Decision Making (MODM). The MADM is for studying the problems having one goal, but more than one criterion (attribute) and ranking alternatives, while the MODM is for investigating problems having more than one goal (objective) and aiming to achieve optimal or aspired goals by considering various interactions within the system. 2 Azapagic and Perdan3, 4 proposed an integrated framework based on MCDM to support decision-making for sustainability. Ren et al.5 proposed an MCDM-based methodology for sustainability prioritization of industrial systems. Serna et al.6 described a multi-criteria decision analysis methodology for the selection of sustainable chemical process routes. Moradi-Aliabadi and Huang7 introduced a sustainability 2 ACS Paragon Plus Environment

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performance improvement methodology that is particularly useful for identifying optimal strategies for multistage sustainability enhancement problems under different objective functions; it can provide well-organized, valuable information for decision makers to make a better decision in their projects. Yue et al.8 proposed a life cycle optimization framework for the design of sustainable product systems and supply chains under economic and environmental criteria. In the work by Guillén-Gosálbez et al.,9 an LCA framework for optimal design of chemical processes shows an approach for studying multi-objective optimization problems. The Collaborative Profitable Pollution Prevention (CP3) idea was introduced by Piluso and Huang, 10 which aids in decision-making for the study of sustainable development of industrial zones. Liu and Huang 11 presented a simple, yet systematic interval-parameter-based methodology for identifying quickly superior solutions under uncertainty for sustainability performance improvement. All these very useful methodologies that are applied at different scales, focus on the first aspect of the definition of sustainable manufacturing. Sustainable development is a dynamic process as the term, “development” implies a directional and progressive change and the term, “sustainable” points to maintaining an improvement over time. Mathematically, the sustainability status of a system can be represented by a vector function, whose element values are determined by sustainability indicators selected for quantifying system performance.12 A system state transition toward sustainability consists of four principal stages and associated activities that are to be investigated, which are system characterization, sustainability assessment, sustainability enhancement, and system adaptation stages.13 In the first stage, the problem scope and context should be defined and a sustainability goal be specified. In the second stage, the current status of sustainability and solution options are evaluated and then, in the third stage, the preferred solution is selected and implemented. In the



last stage, the outcomes are evaluated in order to remain the system on a sustainable trajectory in long term by an adaptive approach. As sustainability is all about development and pathways, this determines that sustainability development (SD) is a class of dynamic control problem, which is non-conventional and sophisticated task. Industrial systems are always interacted each other as well as with the surrounding social and environmental systems. Thus, decision making for sustainability enhancement should be holistic. Newman14 and Fiksel15 stated the importance of gaining deep understanding of the dynamic, adaptive behavior of systems, as steady-state sustainability models are too simplistic. The best way for addressing these issues and deriving comprehensive solutions is to adopt an integrated system approach because the systems view is a way of thinking in terms of connectedness, relationships, and context.13,16 In this paper, we propose a novel sustainability decision-support framework by resorting to sustainability fundamentals, system control science, and engineering science. In this framework, a hierarchical control structure consisting of the strategic control and tactical control layers is designed to assist decision makers in developing short-to-long-term SD strategies and implementing them in supervision. The Model Predictive Control (MPC) strategy is applied as the control approach in the tactical control layer to derive sustainability actions for decision makers. This framework that is underpinned by a systems approach can facilitate the process of better understanding and solving sustainable manufacturing problems and provide a structured guide to decision makers in setting sustainability goal and determining sustainability actions. In the next section, the framework is explained in details, and in section three, an application to a sustainable biodiesel manufacturing problem is presented to illustrate the methodological efficacy.


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Overview of the Framework: Strategic-Tactical Control System

Transition toward sustainability is a continous process that could involve multiple stages. This requires a continuous sustainability assessment, control action, and adaptive goal setting. The decision-support framework that has two control layers is outlined in Fig. 1(a). The upper layer that is for strategic control is designed to assist decision makers in setting a SD goal, while the lower layer that is for tactical control is designed to ensure that the sustainability target derived at the strategic control layer be achieved dynamically. The key component for the strategic control is sustainability goal options generator that provides all the necessary information to decision makers for sustainability goal setting (see Fig. 1(a)). As shown in the figure, the strategic layer uses all the information available at the tactical layer (see the dotted arrows) to generate all the sustainability goal options for decision makers. After the sustainability goal is set at the strategic level, the tactical layer is responsible for achieving the goal dynamically through deriving the optimal sustainability actions using the MPC strategy. The tactical control layer has three key components: (i) the Decision Generator (as a “controller”, where model predictive control is employed, (ii) the Industrial System (e.g., a highly interacted material-energy system), and (iii) the Sustainability Assessor (as a “measurement device”). This control scheme is designed to make the industrial system to follow the pre-set SD trajectory, while experiencing possibly various types of disturbances. All the components of the decision-support framework are explained in details in the following section. First the sustainability goal options generator is explained, and then the tactical control layer with its components are discussed in details. Sustainability goal options generator. Setting sustainability goal is a very challenging task for decision makers because of the multi-objective nature of sustainability problems,



involving economic (E), environmental (V), and social (L) sustainability objectives, which are frequently conflicting. Therefore, we need to have a systematic methodology for generating sustainability goal options for decision makers to set short-to-long-term sustainability goal(s). The valuation model of each of these objectives is discussed in the sustainability assessor block. The sustainability goal options generator formulates the sustainability problem as a MultiObjective Optimization (MOO) problem by using information, such as the current sustainability status, constraints (e.g., budget limit), alternatives for sustainability improvement (decision space), etc. In multi-objective or vector optimization problems there are at least two objective functions and, in general, there is no single optimal solution that simultanously optimizes all the objective functions. In these problems, the concept of optimality is replaced by that of Pareto optimality, which provides the most preferd solution to decision makers, in contrast to the optimal solution. The Pareto optimal solutions (or Pareto set) are the solutions that cannot be improved in any of the objectives without degrading at least one of the other objectives. A Pareto optimal solution, also called non-dominated solution, is defined as follows. Assume that all the objective functions

 fi , i 1, 2,, q are for maximization, a feasible solution x of an MOO problem is a Pareto optimal

   solution, if there is no other feasible solution x  , such as f i x  f i x  i  1, 2,  , q  with at least one strict inequality.17 Figure 2 shows the Pareto optimality concept for a MOO problem with two objectives. The red curve shows the Pareto optimal solutions (Pareto set), if two   objectives are to maximize f1 x  and f 2  x  . All the solutions below this curve are feasible but

suboptimal that can be improved. Any point above the curve is infeasible. This approach provides complete information to decision makers, which can understand the inherent tradeoffs among sustainability objectives. For an MOO problem with three objectives, the Pareto set is a surface in three-dimensional (3D) space. Any solutions on the Pareto curve can be considered as a 6 ACS Paragon Plus Environment

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sustainability goal for future sustainability improvement based on the decision makers’ preference. Any of these solutions in f1-f2 domain is correspondent to an optimal design configuration in the decision space that specifies a specific plan for revamping or retrofitting projects. There are different methods for generating the Pareto optimal solution, such as the weighting method and the ε-constraint method. In this work, the   constraint method17 is adopted; this method optimizes one of the objective function, while the other objective functions are considered as constraints as shown below:

max s.t.

 f1 x   f 2 x    2  f3 x    3

(1)

  f p x    q  xS

Pareto optimal solutions are obtained by parametrical variation on the right-hand side of the constrained objective functions   i  . Here, S denotes the feasible decision space. After setting a sustainability goal at the strategic control level, the tactical control is responsible for achieving the goal by implementing the best sustainability actions. We discuss the tactical control system with its components below. Tactical control. In the tactical control system, model predictive control (MPC) is implemented by following the steps below.



Step 1. Measure the sustainability status of the system, S t  , at each instant t by the sustainability assessor.





Step 2. Derive a set of sustainability actions, U  t   u t t , u t  1 t , , u t  M 1 t  , for the entire time window (i.e., for M stages) through optimizing a determined criterion. For



example, in a terminal target problem shown in Fig. 1(b), our interest is to minimize the error



g between the desired sustaianbility target position, S M  , and the actual sustainability status of



the system, S M  , at the end of the final time M. Different criteria such as minimum cost, minimum time, and maximum efficiency defined by Moradi-Aliabadi and Huang7 can also be considered.

 

Step 3. Implement the first sustainability action, u t t , and reject the rest sustainability

 S actions in the option list, because at the next sampling instant t  1 is already known and Step





1 is repeated with this new value. Thus u t  1 t  1 is calculated that is in general different from

u t  1 t  because new information becomes available. This framework can also be interpreted as the sustainability management system (SMS) that can help companies to evaluate their sustainability performance and drive continuous improvement toward sustainability. This is based on the well-known Plan-Do-Check-Act cycle of continuous improvement shown in Fig. 1(c). In the following section, the components of the tactical control system are explained in details. Decision Makers. The decision makers block is responsible for several tasks, such as defining the scope of a sustainability problem, formulating sustainability strategy that includes specific sustainability goals for the system within a desired time period, and interacting with Decision Generator block (see Fig. 1(a)). Owing to the complexity of sustainability problems, any SD decision derived by the Decision Generator must be approved by the decision makers before implementation. In any circumstances, the decision makers have a right to modify any computerderived decisions, as they serve only as a reference to the decision makers.


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Decision Implementer.

Any sustainability improvement decisions approved by the

decision makers will be implemented by engineers, technicians, and management personnel. The quality of decision implementation will be largely affected by the implementer’s understanding on the decisions, knowledge and skills, responsibility level, on-site implementation conditions, etc. In this work, we assume that all decisions made by the decision makers can be fully and effectively implemented within permitted time. Industrial System. Sustainability problems appeared in industrial systems vary greatly, as an industrial system could be as sophisticated as the one containing many subsystems and interacting not only each other but also with the environmental and social systems. 18 In this study, we focus only on a type of industrial systems that is defined as a collection of materially and energetically connected operations. Depending on how detailed the available data are, the subsystems can represent the unit operations or a group of units. Each subsystem is described in detail by flows of materials and energy as well as emissions (air, water and solid wastes). Sustainability Assessor. The Assessor contains sets of sustainability indicators that are carefully selected for conducting sustainability assessment in different project stages and determine if progress is being made.19 The sustainability status of the system can be evaluated using the sustainability valuation model set shown in Fig. 3, which includes economic, environmental, and social valuation models. AIChE20 and IChemE21 sustainability metrics systems are highly recommended for sustainability indicator selection. Note that a combination of the approaches commonly used for separately assessing economic, environmental, and social sustainability has been also widely practiced,20-27 as this provides flexibility for users to assemble their own sustainability metrics in applications.



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There exist many methods and tools for sustainability assessment for different types of applications. EPA’s GREENSCOPE28-30 is a tool for evaluating process system’s environmental, economic, and energy efficiency. The waste reduction (WAR) algorithm 31-33 is usually used for evaluating the environmental impact of a process in eight environmental impact categories. Piluso et al.34, 35 developed a methodology based on the known Ecological IOA (EIOA) method, 36 which is for analyzing the current and future state of industrial sustainability within an industrial region or zone. For a product system, life cycle costing (LCC)37, life cycle assessment (LCA)38, and social life cycle assessment (SLCA)39 are used as tools for evaluating economic, environmental, and social impact. For large-scale sustainability problems, system dynamics approaches 40 can be used to investigate the dynamic behavior of complex systems in the context of the triple value model proposed by Fiksel et al.13 As sustainability improvement problems involve the direction and the extent of sustainability status change, Moradi-Aliabadi and Huang12 introduced the sustainability vector concept and vector based analysis method. In this approach, a sustainabilty vector is characterized by its magnitude and direction in a triple-bottom-line-based 3D space. The sustainability status of



a system can be described by the vector function, S k  , below:

 S (k )  E ( k ) , V ( k ) , L ( k ) ,

(2)

where F

E (k ) 

 al El ,N (k ) l 1

F

 al

l  1,2, , F

,

l 1


(3)

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G

V (k ) 

 bmVm ,N (k )

m1

,

G

 bm

m  1, 2, ,G

(4)

n  1,2, , H

(5)

m1 H

L(k ) 

 cn Ln ,N (k ) n 1

H

 cn

,

n 1

where El , N (k ) , Vm ,N (k ) , and Ln ,N (k ) are, respectively, the individual normalized economic, environmental, and social sustainability indicators, each of which has a value between 0 and 1, with 0 the worst and 1 the best. The normalized indicator can take the value I N or 1  I N , depending on whether a higher indicator value is more desirable or not. Parameters al, bm, and cn are the weights associated with different indicators, reflecting relative importance of each individual indicator against others in assessment. These weighting factors can be determined by different methods. Equal weights can be used if there is no knowledge about decision maker’s preference and no data or information is available to differentiate the importance of indicators. Weights can be also set differently by a subjective weighting method (based on decision maker’s preference), an objective weighting method (based on data), or a combination of both methods.41 The sustainability status of a system can be changed mainly due to sustainability actions (see Fig. 3), which can be expressed as:





  S k 1  f S k ,u k  ,

(6)

 where u k  is the sustainability action, and S denotes the sustainability status of the system; f is

a vector-based function and index k stands for the sampling time. Decision Generator. A core area of research in sustainability science is the study of system’s “transition toward sustainability”. Kates et al.42 states that the purpose of sustainability



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assessment is to assist decision makers in determining which actions should or should not be taken in an attempt to make society sustainable. The decision generator is another fundamental component of the framework, as it provides sustainability actions by solving an optimization problem. The formulation of this control problem requires: (i) a mathematical description of the system under consideration. (ii) constraints such as budget limit, etc., and (iii) a performance index (objective). When the sustainability status of a system is changed only by input u k  , Eq. (6) can be written as below:  Eˆ k   Eˆ k  1  f ˆ u k       E  Vˆ k    Vˆ k  1    f ˆ u k  ,      V  ˆ ˆ  Lk    Lk  1   f ˆ u k         L

(7)

where f Eˆ u k  , fVˆ u k  , and f Lˆ u k  are the functions that show the impact of sustainability action u k  on the economic, environmental, and social sustainability performances of the system, respectively. Here, we use the hat notation for the sustainability status of the system predicted by the models. There could be various sustainability actions to take in each time instant t for sustainability improvement. The actions with their potential sustainability performance improvement can be summarized in Table 1. The table needs to be updated in each stage as the new options are available for improvement. Different constraints can be included in the optimization problem, such as the minimum requirement of sustainability performance improvement level for each stage, budget limit, minimum internal rate of return (IRR), maximum payback period for projects implementation, etc. For example, an organization may set the maximum payback period to N years for implementation of energy conservation projects, which often involves purchasing or upgrading equipment.43 12 ACS Paragon Plus Environment

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 The optimizer generates the optimal plan U t  by optimizing a determined criterion at

each instant t. In a terminal target problem, the criterion is the weighted Euclidean distance defined as:   T   J   S g M   Sˆ M  W  S g M   Sˆ M  ,    

(8)

where W is a diagonal weighting matrix defined bellow:  w1 W   0  0

0 w2 0

0 0  w3 

(9)

where w1, w2, and w3 are the weights associated to the economic, environmental, and social composite indexes, respectively, which can be determined by a subjective weighting method, an objective weighting method, or a combination of both. By gathering all the terms together, the following optimization model is obtained that should be solved at each sample time t.   T g g    ˆ ˆ argmin J   S M   S M  W  S M   S M      Ut

s .t .

  Sˆ 0  S t  Eˆ k  1  Eˆ k   f Eˆ u k 

Vˆ k  1  Vˆ k   fVˆ u k  Lˆ k  1  Lˆ k   f Lˆ u k 

k  0,1, , M  1 k  0,1, , M  1 k  0,1, , M  1

Eˆ k  1  Eˆ k  Vˆ k  1  Vˆ k 

k  0,1, , M  1

Lˆ k  1  Lˆ k 

k  0,1, , M  1

Cost u k   B up k 

k  0,1, , M  1

k  0,1, , M  1


(10)


In the above model, the equality constraints describe the sustainability status transition of the system, the inequality constraints (  ) show the sustainable development space, and the last constraint is a budget limit for project implementation. In this study, we use an interval parameter method to deal with uncertainties related to raw materials and product pricing, market demand, environmental regulation, etc. Note that a parameter interval can be set based on the analysis of historical data, literature, expert’s knowledge and experience. The sustainability status of the system P can be assessed using the available data collected from the system. Also, it is very possible that technology inventors, providers, and users can provide some technology assessment information based on their tests and experience for new technologies. In the case of missing technical data for some indicators, then a usual approach is to try to obtain them through some pilot experiments, or to use some reliable system simulator(s) to generate reasonable performance data. If these are all not feasible, the sustainability indicator(s) should be excluded from the list of indicators for sustainability assessment. There are many techniques available to solve the above optimization problem. In this work, Genetic Algorithm (GA) is employed, together with a Monte Carlo (MC) technique for handling uncertainties. More information about this optimization technique can be found in the study by Moradi-Aliabadi and Huang7.

Case Study

The introduced framework is used to investigate an alkali-catalyzed biodiesel manufacturing system. The process flowsheet is shown in Fig. 4, and the plant has the production capacity of biodiesel (alkyl ester) 8,000 tons/year and the feedstock is vegetable oil or animal fat.


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The relevant process data and information about the technology can be found in Zhang,44 Zhang et al.,45 and West et al.46 This plant has a number of sustainability problems that are related to energy efficiency, waste reduction, and product quality. It is aimed at improving the plant’s sustainability performance within three years (M=3). The system and sustainability related technical data are adopted from Liu and Huang.11 Note that their data were expressed as interval numbers for considering uncertainties associated with data. The sustainability assessor of the hierarchical control system uses seven indicators from the IChemE Sustainability Metrics system to assess and track the sustainability status of the system. The economic sustainability indicators include: the value added (E1) and the gross margin per direct employee (E2). The environmental sustainability category contains three indicators: the total raw materials used per pound of product produced (V1), the hazardous solid waste per unit value added (V2), and (3) the energy intensity (V3). In the social sustainability category, the selected indicators are: the lost time accident frequency (L1) and the number of complaints per unit value added (L2). The sustainability assessor uses the sustainability valuation model shown in Fig. 3 to assess the sustainability status of the system in each year using the data and information collected from the system. To improve the sustainability performance, a total of 10 technologies have been identified. Among them, four technologies for source waste reduction; they are: T1 – to separate methanol in the waste stream from the glycerol purification column and then to recycle it to the transesterificaiton reactor, T2 – to recycle the unconverted oil as part of the feedstock after pretreatment, T3 – to recycle the waste stream of the glycerol purification column to the liquid– liquid extraction column as a washing solvent to replace fresh water, and T4 – to recovery solid waste from the catalyst removal separator as a type of fertilizer. The remaining six technologies



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are for energy efficiency and product quality improvement; they are: T5 – to redesign the product purification sequence, T6 – to pretreat waste cooking oil as a new feedstock, T7 – to adopt a new catalyst for the transesterification reactor in order to improve its conversion rate, T8 – to recover energy from the glycerol purification process, T9 – to recover energy from the transesterification reaction process, and T10 – to recover energy from the biodiesel purification system. Sustainability performance of process plant P and 10 technology candidates for sustainability improvement are assessed, where system model and sustainability valuation model are used; the assessment results are summarized in Table 2. In the table, the cost data for technology use as well as the time needed for technology implementation are also included (see the last two rows of the table). Any individual technology or a combination of some of them are the potential sustainability improvement actions, which can be summarized using the structure of Table 1. The decision generator needs to identify the optimal technology sets for implementation g in different stages in order to achieve the preset sustainability goal, S M  , in the end of stage M.

In this work, the sustainability performance improvement methodology developed by MoradiAliabadi and Huang7 is used. Based on their methodology, functions f Eˆ u k  , fVˆ u k  , and

f Lˆ u k  can be represented by the following equations: f Eˆ u k    y k, j ΔE T j , P 

(11)

fVˆ u k    y k, j ΔV T j , P 

(12)

f Lˆ u k    y k j ΔLT j , P 

(13)

N

j 1

N

j 1

N

j 1


Page 17 of 46 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


where E (T j , P ) , V (T j , P ) , and L(T j , P) are, respectively, the economic, environmental, and social sustainability improvements of system P when technology Tj is adopted; y k , j is a binary variable that is defined as follows:

1, yk , j   0,

if technology j is selected at year k otherwise

k = 0, 2, …, M-1; j = 1, 2, …, N (14)

There are several logical constraints associated with this optimization model that we don’t discuss them here for the sake of brevity. Details about the constraints and the methodology can be found in Moradi-Aliabadi and Huang.7 The parameters of the optimization model are summarized in Table 3. The budget limits are $300k, $200k, and $100k for the first, second and third stages, respectively. f ζ k  is a function showing a discount if more than one technology is used at the kth stage, where parameter  k is the number of technologies adapted. The identity matrix that shows the equal importance of three dimension of sustainability is considered for matrix W. Results and analysis. The sustainability goal generator uses the initial sustainability status

 of the industrial system, S 0  , assessed by the sustainability assessor at t  0 , and the constraints such as budget limits and logical constraints that define the decision space of the sustainability problem, to generate the Pareto optimal surface. In this case, the initial sustainability status of the

 system is S 0  0.50, 0.40, 0.35 . As we stated before, we use the ε-constraint method to generate the Pareto set. We set the economic objective as the primary objective and consider the environmental and social objectives as the constraints. By parametrical variation in the right-hand side of the constrained objective functions (ε1 and ε2) and solving a sequence of single objective optimization problems, the Pareto optimal solutions are obtained. The sustainability cube introduced by Piluso et al.34 is then used to show the Pareto surface, sustainability status of the



system, and state transition process. In the sustainability cube, the three edges are separately assigned to quantify the composite economic, environmental, and social sustainability indices. The Pareto front (Pareto optimal solutions) is shown in Fig. 5, which provides a complete picture of the potentially optimal solutions for the decision makers to set the most acceptable sustainability goal. As shown in Fig. 5, the composite economic sustainability index value is increased from 0.58 to 0.78; the composite environmental sustainability index ranges from 0.54 to 0.66, and the composite social sustainability index is changed from 0.45 to 0.54. Any point on this surface could be chosen as a short-to-midterm sustainability goal by the decision makers. Any point in the space above this surface is an infeasible solution and should not be selected as a sustainability goal, while all the points in the space below the Pareto surface are sub-optimal. As we can see, increasing the value of one objective will be at cost of the other two objective. Thus, the decision makers need to consider trade-off among them. After setting the sustainability goal by the decision makers at the strategic control layer, the tactical control system is responsible to reach this goal. Here, the g decision makers set the final goal for the categorized sustainability to S M   0.70 , 0.6, 0.50 

that is located on the Pareto surface.

 The initial sustainability status of the system, S 0  , is measured and used as a feedback to the Decision Generator to generate an optimal policy for the next three years. As stated before, the

 initial sustainability status of the system is S 0  0.50, 0.40, 0.35 for this case. At t  0 , the  Decision Generator outputs the optimal policy, U t  0   T2 , T4 , T5 , T8 , T1 , T7 , T9  , for the

next three years by solving the terminal optimization problem shown in Eq. (10). The prediction of sustainability status of the system after implementation of this optimal policy is shown in the Fig. 6. As shown, the plant can reach its preset sustainability goals for economic (Fig. 6(a)), environmental (Fig. 6(b)), and social (Fig. 6(c)) sustainability goals after three years. The 18 ACS Paragon Plus Environment

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investments for taking this sustainability actions shown in Fig. 6(d) are $289k, $190k, and $60k in the first, second, and third years, respectively. If the plant operation is not affected by the environment and other conditions, the sustainability actions will not be changed in the next sample times based on the principle of optimality. In this case, the predicted sustainability status of the system will be same as the actual one. Figure 7 shows the initial sustainability status, the system state transition path, and the final sustainability status of the system. Based on the MPC strategy, the first sustainability action, T2 ,T4 ,T5 ,T8 , is implemented at

t  0 and the other two sustainability actions, T1 , T7  and T9  , are rejected. After the first stage of sustainability enhancement, the technology set, T2 ,T4 ,T5 ,T8 , is removed from the table and

 S the next sustainability status of the system, 1 , is measured, which is the system information feedback to the Decision Generator. If the system doesn’t experience any disturbances, the



measured sustainability status of the system, S 1 , would be same as the predicted sustainability status of the system, i.e.,

 Sˆ 1  0.56, 0.52, 0.42 , and the same optimal policy,

U  t  1  T1 , T7 , T9  , will be obtained at the second stage for the next two years based on the principle of optimality. However, if there is a disturbance (for example, changes in demand or product price), the optimal policy for the next two years might be different. Here, we assume that there is a disturbance v   0.02, 0, 0 that perturbs the sustainability status of the system. Based  on the MPC strategy, the new sustainability status of the system becomes S 1  0.54,0.52,0.42 ,

which is then sent to the Decision Generator as an adjusted system status, which requires a review of the previously generated optimal policy for the next two years; in some cases, a new optimal policy needs to be generated. In this case, however, the previously generated optimal policy,



U  t  1  T1 ,T7 ,T9 , doesn’t need to be changed, as the sustainability status of the system resulted from taking these actions under the given budgets still has the minimum weighted Euclidian distance from the preset sustainability goal. However, the economic sustainability goal is not reachable under these budget limits. As we discussed in section 2, the decision makers interact with decision generator at each stage of sustainability enhancement process. They analyze the results generated by the Decision Generator and consider different scenarios to see how the optimal policy is changed in order to make a final decision. For example, the decision maker may want to know if they want to reach the economic sustainability goal (0.70) in the past scenario (in case of existence of disturbance), how much they need to invest more and whether the optimal policy would be changed or not. In this scenario, the results show that the final sustainability goal is achievable, if the budget limit be increased from $200k to $300k for the second stage. The new optimal policy would be

U  t  1  T6 , T9  that is different from the previous one. Implementation of this optimal policy  leads to the final sustainability status of S 3  0.70, 0.59, 0.51 . As the results show the optimizer generates a plan that focuses more on economic sustainability than before by choosing technology T6 that has the highest economic sustainability improvement potential. The proposed methodology is systematic and can be applied in two-dimensional space (e.g., economic (E) vs environmental (V)), if the valuation of the other dimension (e.g., social (L)) is difficult due to a lack of data. In order to show this, we use the same approach, ε-constraint method, to generate the Pareto optimal solutions. In the case of only economic and environmental sustainability (labeled as E and V) can be measured, the introduced methodology is also used successfully to derive solutions, which are summarized in Table 4. Any of these solutions can be considered as a plan for future sustainability improvement of the biodiesel manufacturing process. 20 ACS Paragon Plus Environment

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The finial sustainability status of the system and the overall sustainability that is defined as the norm of sustainability status vector are shown in the third and fourth columns of the table. As the results show, the overall sustainability of the different plans is almost the same with average difference of less than 0.02. However, the range of each composite sustainability index is significant. The Pareto front curve in E-V space is plotted and shown in Fig. 8. Trade-off opportunity between economic and environmental objectives is depicted in the figure. The Pareto optimal solutions are shown on the curve that are marked as P1 to P6. Any of these points can be selected as a sustainability goal. For instance, the sustainability goal can be set on points P1 or P2, if the plant wants to emphasize more on the economic sustainability; otherwise points P5 or P6 can be chosen for more emphasis on the environmental sustainability. The points P3 or P4 can be selected for more balanced sustainability performance improvement. Note that all the points above the Pareto curve are infeasible solutions; the points below the curve are feasible but suboptimal.

Discussion

Industrial practice has shown that when an industrial organization plans to develop strategies for achieving a short, medium, or long-term sustainability goal, it must make best possible assumptions based on their prediction of, for example, market potential, raw material and energy cost, environmental, health and safety related regulations, for the planning period. Their predictions are definitely rough, and most practically are expressed as interval numbers; note that due to no sufficient data for future situations available, no probability distribution functions could be ever established. In this work, we use the interval parameter method discussed at the end of section 2 to handle uncertain information. Our industrial collaborators in different companies



support this interval parameter method; they think this is a practical approach for industries to use, as they could provide the estimates of the lower and upper limits of parameters. Sustainability performance of an organization can be improved in many ways, such as at the management level and at the technical level. The technical approach for sustainability improvement mostly focuses on the evaluation of the technologies already being used and those under review for possible adoption (including those emerging and well established). Technologies are developed to show their specific technical functions. However, many of them, either already being used in plants or under consideration for adoption, need to be evaluated comprehensively not just for their technical performance and cost, but also for the potential environmental and social impact. The sustainability assessment method described in this manuscript is general and quite straightforward for evaluation of any individual or a group of technologies in different decision stages under budget or some other considerations. The only challenge in technology evaluation is how to ensure that the evaluation is well acceptable. There are a number of approaches for this purpose. For the technologies under consideration, companies should request the economic, environmental and social performance data from technology vendors. If a project is contracted to other company or consulting firm, they should obtain these data from the technology vendors; they could also do either pilot testing or analysis using commercial simulators. All those are usual industrial approaches. Once the data are available, either precise ones (expressed by numbers) or imprecise ones (expressed by interval numbers), they can be readily used in our sustainability control method, which is general for any type of application. In principle, the proposed framework should be applicable to the sustainability improvement problems of a system of any (time and length) scales, where the system boundary is clearly defined. For a system beyond a process or plant scale, decision variables will be beyond


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technologies. For an ecosystem sustainability problem, any measures potentially valuable could be the candidates of decision variables. The sustainability impact of these measures should be evaluated using the selected sustainability indicators first. The proposed sustainability assessment and strategic and tactical control method for solution derivation should be applicable, except for different types of systems, the system characterization and specific sustainability indicators, as well as decision variables are different from those used for a process plant sustainability problem.

Concluding Remarks

Sustainable manufacturing is a multi-objective optimal control problem, as it involves a series of system state transitions toward a specified goal in the economics-environment-societybased sustainable development space through implementing sustainability improvement actions. However, setting sustainability goals and planning for reaching those goals are very challenging tasks. In this paper, we introduced a general decision-support framework for sustainable manufacturing by resorting to sustainability fundamentals, system control science, and engineering science. This two-layer hierarchical control framework is designed to support decision makers. A multi-objective optimization approach is applied to generate sustainability goal options at the strategic control layer, which facilitates the goal setting process for decision makers. The tactical control layer, on the other hand, is constructed based on the model predictive control (MPC) strategy to derive optimal sustainability improvement strategies and implement them in a dynamic domain. This hierarchical decision-making framework can provide a structured step-by-step guide for industries to define sustainable development strategies and derive sustainability actions systematically. The proposed methodology is general and thus it can be applied to any industrial sustainability problem. For a grassroots design problem, we can use the proposed methodology to 23 ACS Paragon Plus Environment


evaluate if its sustainability performance can be improved, and if yes, what the best strategy could be. For a retrofitting problem, the methodology can be used to study if the system modification is more sustainable than before or not, or if there is any better way to make the system more sustainable. The case study on a biodiesel manufacturing problem has demonstrated the efficacy of the methodology.


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Nomenclature Bj

cost for adopting technology Tj

Bup

budget limit

D

Disturbances

E

categorized economic sustainability index

F

total number of economic sustainability indicators

f

objective function

G

total number of environmental sustainability indicators

H

total number of social sustainability indicators

k

discrete time that shows the k-th stage of sustainability development

L

categorized social sustainability index

M

planning window

P

plant

 S

sustainability status vector of the system

 Sˆ

estimated sustainability status of the system

S

feasible decision space

T

technology

t

time

U*

optimal plan at time instant t

u

sustainability action

u*

optimal sustainability action

V

categorized environmental sustainability index

 x

feasible solution



W

weighting matrix

y

binary variable

Greek 

weighting factor of the categorized economic sustainability index



weighting factor of the categorized environmental sustainability index



weighting factor of the categorized social sustainability index



constraint parametrical variation

Subscripts a

weighting factor of economic sustainability indicators

b

weighting factor of environmental sustainability indicators

c

weighting factor of social sustainability indicators

i

index of objective functions

j

index of technologies

l

index of economic sustainability indicators

m

index of environmental sustainability indicators

n

index of social sustainability indicators

q

index of objective functions

Acknowledgment

This work is supported in part by NSF (Award No. 1437277 and 1604756).


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Table 1. Sustainability actions with their sustainability improvement potential Sustainability Development Vector Sustainability Actions

Cost ($) Time

f Eˆ u k 

fVˆ u k 

f Lˆ u k 

1

E1

V1

L1

B1

t1













m 1

E m1

Vm1

Lm 1

Bm 1

t m1

m

E m

Vm

Lm

Bm

tm


ACS Sustainable Chemistry & Engineering 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

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Table 2. Index-specific sustainability assessment of the system and candidate technologies Category Econ. (E)

Environ. (V)

E1

System P [0.550, 0.570]

E2

0.450

V1

0.400

V2

[0.350, 0.380]

V3

0.420

Index

[0.335, 0.340] [0.370, L2 0.380] Cost for technology use B(Tj) (K$) Time for technology use tj (month) Soc. (L)

L1

Technologies in Group 1 T2 T3 T4 [0.600, [0.570, 0.620 0.540 0.620] 0.580] [0.500, [0.460, [0.440, [0.470, 0.510] 0.480] 0.450] 0.490] [0.430, [0.420, 0.430 0.450 0.450] 0.430] [0.360, [0.400, 0.380 0.380 0.380] 0.420 [0.420, 0.430 0.400 0.410 0.430] [0.355, 0.340 0.340 0.350 0.360] [0.378, [0.380, 0.400 0.380 0.380] 0.385] T1

T5 [0.560, 0.580] 0.470 [0.460, 0.470] [0.380, 0.390] 0.440 [0.310, 0.315] 0.400

T6 [0.640, 0.660] [0.560, 0.580] [0.480, 0.500] [0.380, 0.400] [0.420, 0.430] [0.370, 0.800] [0.420, 0.430]

Technology in Group 2 T7 T8 [0.530, 0.580 0.550] [0.440, 0.440 0.450] [0.430, 0.470 0.440] [0.400, 0.400 0.410] [0.470, [0.440, 0.480] 0.460] 0.350

0.440

[0.380, 0.390]

[0.360, 0.370]

T9 [0.590, 0.610] [0.520, 0.530] [0.410, 0.420] [0.350, 0.380] [0.420, 0.430] [0.390, 0.400] [0.400, 0.410]

T10 [0.530, 0.550] [0.440, 0.450] [0.460, 0.470] [0.400, 0.410] [0.430, 0.450] [0.350, 0.365] 0.370

100

50

50

80

120

300

100

90

60

80

3

1

1

2

3

8

1

2

2

3


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Table 3. Model parameters used in optimization model Parameters

Year 1

Year 2

Year 3

300

200

100

Budget limit(K$) Weighting factor of selected Indices

a1  1, a2  1, b1  2, b2  1, b3  1,  c1  3, c2  1

Weighting factors

w1  1, w2  1, w3  1

Discount function

f     0.05  1.05

Total number of candidate technologies

N=10



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Table 4. Pareto optimal solutions in E-V space Plan

Final sustainability state vector

Pareto optimal solution

Overall sustainability  S

P1

T6  , T1 ,T2 ,T9  , T4 

   S1  0.78 i  0.54 j

0.67

P2

T6  , T2 ,T7 ,T9  , T1

   S 2  0.76 i  0.57 j

0.67

P3

T6 ,T2 ,T7 ,T9 ,T10 

   S3  0.71i  0.60 j

0.66

P4

T2 ,T4 ,T5 ,T8  , T1 ,T7  , T9 

   S 4  0.68 i  0.62 j

0.65

P5

T5 ,T7 ,T8 ,T1 ,T2 ,T9 ,T10 

   S5  0.65 i  0.65 j

0.65

P6

T3 ,T4 ,T5 ,T7 ,T2 ,T9 ,T10 ,T8 

   S6  0.60 i  0.66 j

0.63


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Figure 1. Decision-support framework for sustainable manufacturing: (a) a strategic-tactical control scheme, (b) a terminal target problem, and (c) a Plan-Do-Check-Act cycle.



 f1  x 

x2

Pareto Set

f1max x

Feasible Decision Space

Infeasible Solutions

 F x 

   F  x    f1  x , f 2  x 

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Feasible Solutions

x3

x1

 f2 x 

f 2max

Figure 2. Pareto set - feasible decision space and Pareto frontier for a two-objective optimization problem.


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Figure 3. Sustainability valuation model.



Oil, Methanol, …

Energy

Wastes

Water

Biodiesel

Figure 4. Industrial system boundary of an alkali-catalyzed biodiesel manufacturing process.


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Figure 5. Pareto surface generated by the sustainability goal options generator.



Figure 6. Prediction of sustainability status of the system: (a) Economic Sustainability, (b) Environmental Sustainability, (c) Social Sustainability, and (d) Investment for optimal policy implementation.


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Figure 7. Sustainability status of the system and sustainability status transition path of the system in a 3D sustainable development space.



Figure 8. Pareto optimal solutions and sustainability development path in a 2D sustainable development space.


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A novel sustainability decision-support framework is introduced for enhancement of manufacturing sustainability by resorting to sustainability fundamentals, system control science, and engineering science.

For Table of Contents Use Only.


Strategic Layer

ACS Sustainable Chemistry &Sustainability Engineering Page 46Goal of 46 Decision Makers

! Sg

Options Generator

1 Reference 2 (Sustainability goals) 3 Industrial Sustainability System 4 Decision Generator Action (Input) 5 ACS Paragon Plus Environment (Optimizer) 6 Sustainability Assessor 7 Tactical Layer

Decision Support for Enhancement of ... - ACS Publications

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