Article pubs.acs.org/IECR
Development of an Agent-Based Modeling Methodology for an Industrial Byproduct Exchange Network Design Hyunjoo Kim,† Jun-hyung Ryu,*,‡ and In-Beum Lee† †
Department of Chemical Engineering, POSTECH, San 31, Hyoja-Dong, Nam-gu, Pohang, South Korea, 790-784 Department of Nuclear and Energy Systems, Dongguk University, Seokjang-dong, Gyeongju, South Korea, 780-714
‡
ABSTRACT: Byproduct exchange networks are attracting increasing attention to allow the use of byproducts that would have otherwise been discarded. To take full advantage of the benefits, it is necessary to consider the interacting features between the participating byproduct buyers and sellers in terms of both economic and environmental perspectives. This paper proposes an agent-based modeling framework as an alternative. A case of water reuse networks in an iron and steel manufacturing industrial park is presented to illustrate the applicability of the proposed framework.
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INTRODUCTION The reuse of byproducts is attracting increasing attention as a methodology to increase the competitiveness of process operations. The byproducts generated during a process are generally of no value for the process itself and incur further disposal cost. Reusing them is beneficial in terms of both economic and environmental perspectives because (i) some of them may be still valuable to other processes within the same company or in other companies and (ii) reusing waste materials can reduce the total emissions of environmentally hazardous materials. Focus has been given to connecting as many byproduct supplies and demands as possible. The resulting byproduct exchange (BPX) network is becoming an important issue.1,2 A BPX connection between a seller, who is byproduct supplier, and a buyer, who has a demand for a byproduct, is only made when both sides agree on the exchange conditions, such as price, amount, and quality, etc. In particular, a key issue in developing a byproduct exchange (BPX) network is to evaluate its environmental impact on the surroundings. Before using the byproducts of other companies, it is important to know who is going to be responsible for the environmental burden. In BPX networks, there are many participants who find or wait for other partners to exchange information, such as their amounts and concentration of materials. A firm is a major participant of a network whose goal is to maximize its profit. When we say that a firm or a group of firms are improved environmentally, it is not enough to show how much their waste deposits have decreased. Outlining the additional environmental responsibility and the firm’s capability to address them is equally important. Therefore, multiple economic and environmental objectives should be considered simultaneously when constructing a BPX network. The resulting decision-making model for the network should address these multiple and potentially conflicting perspectives as realistically as possible, which is quite challenging for the traditional approach. BPX networks have been studied extensively over the past decade mainly in the context of equation-based modeling.3−6 Process information, such as the raw material demand and waste discharge of participants, as well as their possible linkages, are transformed into equations with the formulation of a © 2012 American Chemical Society
mathematical model. The resulting decision-making model was then optimized mathematically under a certain objective function. Although this typical equation-based optimization model has produced meaningful results for a BPX network, it clearly shows a limitation in reflecting the aforementioned practical situations. Initially, it only considers the main variables determined by an objective function (freshwater demand, wastewater discharge, total cost, etc.). The model focuses on maximizing the total benefit of the entire network without considering the individual characteristics. Although there have been few studies dealing with the issue of individual relationships, for example, using game theory,7 there is a motivation to develop an alternative methodology to address a BPX from a practical point of view. This paper proposes an agent-based modeling (ABM) approach for the design of BPX networks. This methodology takes the form of a bottom-up approach in that the entire system is modeled as a kind of automated market where their resources are exchanged in the context of strategies and conditions.8 The remainder of this paper is structured as follows: After the key issues of the byproduct exchange network are outlined, the proposed methodology is adopted to a case of developing wastewater reusing networks in an iron- and steel-making industrial park. Discussion of the case result is followed with some remarks.
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WASTEWATER REUSE NETWORK IN AN ECOINDUSTRIAL PARK Many developing and developed countries including South Korea have industrial parks or complexes with the aim of promoting large-scale heavy industries, such as petrochemicals, iron, and steel. As environmental protection has become a serious issue, the accumulated nature of traditional industrial parks has evolved into a concept of an ecoindustrial park (EIP) denoting a community of multiple manufacturing and service Received: Revised: Accepted: Published: 10860
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businesses seeking enhanced economic and environmental performance through collaboration in managing resources including energy, water, and materials.9 Business communities in EIPs seek a synergic benefit greater than the sum of the individual benefits each company would have by working together. The aim of constructing EIPs is to improve the economic performance of the entire participating companies while minimizing their environmental impact. A range of utilities need to be prepared for the operation of EIPs, such as water, electricity, human resources, transportation, and waste disposal facilities. In particular, securing an adequate water supply in terms of quality, quantity, and cost is essential because it plays a critical role in most production process. On the other hand, a sufficient supply of water is becoming increasingly difficult due to global climate change and the increasing population. Therefore, a wastewater reuse network should be introduced to reduce the overall freshwater consumption and emission of hazardous materials in EIPs. A decision-supporting tool should be developed for the design and operation of the network, mostly by modeling the network. In modeling a wastewater reuse network, it is important to incorporate many practical issues, such as the presence of multiple products and the range of firms whose interests possibly conflict under stringent environmental regulations. Table 1
Figure 1. Agent-based system designs and agent architecture.
which is essential when simulating changes in the market by automated market participants. The main points of interest are the changes and reactions of the individual participants to the needs of environmental improvements. The automated market model is useful for estimating the characteristics of the market and its reaction to changes, particularly when the participants in the system show considerable diversity.8 An automated market in a large framework generally includes market interfaces, trading protocols, and strategies of the participants. Each agent repeats the evaluation, proposal, calculation, and approval steps. Several external standards exist, but there is no external director that gives orders and approvals. A participating agent must decide what information to take and which method to select. Each participant can access all the information in the system, but only upon a direct request. This means that the agents in the system do not have the same level of information. Figure 2 shows the general design procedure in an agent-based system model.
Table 1. Firm Properties Influencing the Environmental Decisions in EIPs10 firm property firm size accounting condition investment tendency product type
influence Large firms discharge more pollution; Large firms tend to have a positive attitude in adopting environmental technologies and equipment If the debt ratio of a firm is high, it shows a negative attitude to introducing environmental equipment If the investment ratio of a firm is high, it actively introduces environmental technologies and equipment When the pollution level of byproducts is high, a firm has a positive attitude to environmental technologies and equipment; A firm whose products are close to consumer goods shows active behavior in adopting environmental technologies and equipment
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WASTEWATER BYPRODUCT EXCHANGE NETWORK DESIGN USING AGENT-BASED MODELING Using the methodologies mentioned above, a wastewater reuse network was emulated as a market dealing with their resources. As shown in Figure 1, the proposed agent-based model includes the agents that represent the individual firms. The agent has not only water inflow and outflow data but also the trading strategies and information that can affect the environmental decisions. Buyers and sellers in the system are not separated. Each firm is allowed to trade its resources without any regulation of its role. Active trading strategies like trading arbitrage were not considered because wastewater is considered a byproduct of production. Table 3 provides the key information on the ecoindustrial park with the basic cost parameters. The initial environmental expense, oci,t, is the sum of the freshwater purchase cost and wastewater discharging cost, as shown in eq 1.
summarizes the properties affecting the environmental decisions of the participants in EIPs.10 Alternatively, a modeling methodology can be employed, but a simplified description of a typical equation-based model might not be sufficient to represent the underlying interrelations of the EIP participants. The next section proposes an agent-based methodology to deal with this problem. To address the combined behavior of individual parts in this way, an agent-based-modeling (ABM) paradigm was recently proposed and proven to be useful for a range of problems in physics and chaotic attractors, biology, economics, social science, and geospatial simulations.11 An ABM is an effective framework for addressing the combined behaviors of individual parts within a single system. The first task in modeling a system using ABM is to analyze the entire system and divide it into several parts, called agents. Each agent has its own role, characteristics, and aim. The agents are then combined to represent the system as a whole. The complex behaviors of systems can be emulated easily by ABM.11 In using ABM in the field of computational economics, there is increasing interest in agent-based automated markets (Figure 1). The huge development of computational technology makes it possible to handle enormous amounts of data in a short time,
oci , t = Pfwqiin, t + Ptrqiout ,t
(1)
The purchase cost is a multiplication of the unit freshwater cost and process water flow rate, and the discharge cost is that of the unit wastewater treatment cost and wastewater flow rate. When a new reusing opportunity is given, only those participants who can afford the additional cost will accept the proposal. A firm with an environment-friendly policy would be ready to incur additional environmental expense. The situation takes the form of multiobjective optimization. Each firm or agent strives to reduce both the environmental influence on the surroundings and the expense of waste. When there is some conflict between them, the agent chooses the more favorable. The environmental consideration of a firm is affected largely by 10861
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Figure 2. Agent-based system modeling procedure.
its size, investment tendency, and product type of the firm, as summarized in Table 1.11 A participant in the system can choose its environmental behavior from the following three: to develop its own treatment center, buy regenerated water, or do nothing. The treatment center requires considerable fixed setup costs. On the other hand, once it is constructed, it effectively reduces the environmental expenses of the firm, particularly when handling high wastewater concentrations. The treatment facility with a stepwise cost function can be formulated using eq 2. fci =
∑ PkIi ,k k
∑ Ii ,k ≤ 1 k
The cost of using regenerated water can be calculated using eq 3 and the final expense can be derived from eq 4. ⎛
rw ⎜
)
rci , t = P ⎜∑ qjex, i , t + qiex, t ‐ final + ⎝ j if t = 0, rci , t = 0 = 0
∑ Dj , irj , i ,t j
(3)
where the first and second terms denote the regenerated water flow rate cost and piping cost for moving regenerated water between firms. tci , t = oci , t + fci + rci , t
(2)
(4)
where tci,t denotes the total expenses of firm i at stage t, and is the sum of the environmental expense, capital cost of a treatment facility, and expenses of firm i at stage t using regenerated water. Figure 3 describes the overall behavior of an agent in a single stage. The entire system takes the form of an exchanging market, including the firms with their own trading strategies and trading rules prescribed by the market manager. The procedure in agentbased modeling can be explained as follows: when a new stage begins, a firm calculates its new expense and score based on its own economic and environmental conditions. The expenses calculated from eq 1 representing the total current economic cost are not enough to make a decision on the network. The score combines the firm’s general characteristics, such as water flow quantity and environmental conditions. Therefore, the expense and score become a guideline for the firm’s behavior in the current stage: they are involved in marginal calculation and environmental behavior selection. The firm then considers
where fci denotes the capital cost of a treatment facility with the size level k, Pk is the capital cost of a treatment facility of firm i, and Ii,k is a binary variable representing whether firm i selects a treatment facility with a size level of k. Selling regenerated water to other participants is also economically feasible. Buying regenerated water is a passive method compared to the construction of a treatment center. From a firm’s perspective, environmental improvement is generally not significant. On the other hand, the use of regenerated water produces considerably more environmental improvement to the entire system because water consumption and wastewater discharge are reduced dramatically without considerable expense.4 The system manager therefore reduces the cost of the regenerated water by giving a subsidy or remitting tax to promote the participants to adopt this method because of the potential cost reduction of water by using regenerated water. 10862
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Figure 3. Flowchart of an agent behavior.
is received, the agent checks its propriety and decides whether to accept it. The stage is then updated until the entire market is stabilized.
whether to propose a new environmental behavior, chooses an appropriate method, and transfers the suggestion and information to a possible partner if needed. When a suggestion 10863
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Figure 4. Initial structure of the case study system prior to developing the wastewater reuse network.
Table 2. Water Usage Data of Industries in the Case Studies freshwater input
a
wastewater discharge
firm ID
quantity (ton/h)
cost (cu/h)a
quality level (ppm)
quantity (ton/h)
cost (cu/h)
quality level (ppm)
priority Si
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10
162.5 9.36 91.32 18.83 214.61 29.17 0 58.33 12.95 0
97.5 5.616 54.792 11.298 128.766 17.502 0 34.988 7.77 0
325 18.72 182.64 37.66 429.22 58.34
152.08 18.71 32.53 12.63 16.67 17.92 0 50 12.96 0
38.02 4.6775 8.1325 3.1575 5.8345 6.272 0 12.5 2.592 0
760.4 84.195 162.65 56.835 100.02 107.52
2.3848 4.6264 2.1228 1.5723 2.2567 2.3656 0.0369 1.9750 1.7630 0.0091
116.66 25.9
225 45.36
cu = cost unit.
In the next section, a case study will be presented to illustrate the suitability of the proposed modeling framework.
(score 1)i , t =
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CASE STUDY Consider a wastewater reuse network in an iron and steel industrial park consisting of ten plants, as shown in Figure 4. Each firm in the park has certain water demands and wastewater discharges represented by a sum of quantity and quality. Here, the quality was evaluated in terms of the chemical oxygen demand (COD) and suspended solid (SS). Discharged wastewater was assumed to have a constant quality with time. Table 2
water cost (cu/t)
abatement: cost (cu/t)
(score 2)i , t =
0.6
0.25
regenerated water
capacity
cost (cu/h)
piping cost (cu/ distance)
water cost (cu/t)
1.67 16.7 167
1.67 8.33 25
0.01
0.3
qiin, t
+ Si (5)
where qout i,t denotes the wastewater flow rate from firm i at stage t, qini,t is water flow rate into firm i at stage t, and Si represents the general characteristic constant of firm I, which is derived from the firm size, R&D ratio, and product type. On the other hand, eq 6 pays more attention to the total contamination and wastewater concentration.
Table 3. Water Purchasing, Abating, and Treatment Cost treatment center
qiout ,t
out Ciout , t qi , t
Ciin, tqiin, t
+ Si (6)
where Cini,t denotes the water concentration input into firm i at stage t, and Cout i,t is the wastewater concentration released from firm i at stage t. By comparing the results using these different scoring methods, it is possible to determine the resulting change in the reuse network caused by the changes in the attitudes of each participant. Although the water quantity in terms of scores 1 and 2 represent each firm’s direct environmental burden caused by freshwater use and wastewater discharge, respectively, the constant S included in both scoring rules interprets the general properties of the firm that affect its environmental decisions, particularly on accepting wastewater reuse. On the basis of the behavioral tendencies listed in Table 1, the properties were converted to a single constant affecting the firm’s score and are presented as a “priority” row in Table 2. This is a simple methodology of reflecting the intentions of the manager of the water reuse system. In the system, there is no overall network supervisor.
lists the freshwater demand and discharged wastewater characteristics. The overall objective was to construct a proper reuse network under given water demand and wastewater discharge conditions. The main concern is to identify the differences in the final network when the agents modify their characteristics. A scoring method was selected as a variable, which means that the attitudes of the agents differ according to their environmental and economic objectives. The following two scoring rules were introduced as an illustration. Equation 5 emphasizes the amount of water use of a participant but does not consider the waste concentration. 10864
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Figure 5. Wastewater reuse network in its development stage according to the first scoring method (score 1).
Figure 6. Final wastewater reuse network according to the first scoring method (score 1).
Figure 7. Wastewater reuse network in its development stage according to the second scoring method (score 2).
Figure 8. Final wastewater reuse network according to the second scoring method (score 2).
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RESULTS OF CASE STUDY The case study was addressed by using the proposed ABM methodology implemented in Matlab. The participants who managed to construct their own treatment centers become core agents and construct a reusing network combining the other agents. The network structure is also changing with this
successive computation procedure of facility introduction, exchange offers, and responses among participants. As the stage is updated, the network configuration at each stage changes with the corresponding decisions, such as freshwater consumption. For example, Figures 5 and 7 give an example of a network configuration during stage processing according to the 10865
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The calculation times to a stabilized stage were 9.4 and 9.57 s for the first and second scoring methods, respectively, with less than 1 s per stage in both cases. The results of this case study are discussed. Initially, the final network structures show significant differences according to different scoring methods, as shown in Figures 6 and 8, although the difference in total freshwater consumption is relatively small. The different network configuration is caused mainly by the different score type. Because score 2 places more weight on wastewater quality, the environmental behavior is more preferred according to this score type. This difference in the score type enforces the network to make new center agents and subnetworks. In other words, the behavioral changes of an agent also appear to be responsible for the entire network configurations. Moreover, agent P1, who produces a considerable amount of wastewater, takes an active position despite its tight capital and affects the network structure directly. Third, an economic evaluation was performed for both cases. The total expense of the system in each score method was calculated, as summarized in Table 4 and Figure 11. The cost of
different scoring methods. When the overall reusing network is finalized after being stabilized, the freshwater consumption and wastewater discharge reach the minimum level. Figures 6 and 8 show the final networks after stabilization. Figure 9 shows graphically how the key feature of freshwater consumption changes at each computation stage. Figure 10
Figure 9. Improvements in the freshwater consumption as a function of the stage using the proposed ABM methodology.
Figure 11. Total expenses of each firm in the water reuse networks.
water use (freshwater and/or regenerated water) and wastewater treatment decreases as the water reuse network develops. The total cost and operational cost from the network according to score 2 is lower than that according to score 1 due mainly to the lower freshwater consumption. The total cost of the reuse network generated using the first scoring method exceeds the total cost of the system without wastewater reuse. Typical mathematical optimization with the aim of minimizing cost would not be feasible here. This suggests that agent P2, which showed the highest environmental priority at the beginning, is willing to bear the additional capital expenses to gain an environmental advantage. This shows the main characteristics of the proposed ABM approach allowing the participants’ tendencies to be adopted without modifying the entire model.
Figure 10. Reduction ratio of the water input and output of the entire system according to two different scoring methods using the proposed ABM approach (100%: freshwater consumption/wastewater discharge in a system without reuse).
presents the final result of employing ABM for a reuse network design; total freshwater amount decreased to 81.7% (score 1) and 74.8% (score 2) of the initial consumption, and wastewater discharge decreased to 75.4% and 52.1%, respectively. The simulations were carried out without any change except for the scoring equations. The simulations were performed with an Intel Core2 2.40 GHz CPU and 2GB RAM using MATLAB. Table 4. Total Expenses of the Water Reuse Networks case
freshwater consumption (cu/ h)
regenerated water consumption (cu/ h)
wastewater discharge (cu/ h)
capital investment (cu/ h)
total expense (cu/h)
no reuse score 1 score 2
358.242 311.898 268.056
0 23.172 45.093
78.375 65.565 62.9475
0 50 50
436.617 450.635 426.0965
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constructed by exchanging information on the wastewater reuse conditions. Because of the decentralized nature of agentbased modeling, this method does not have any supervisory decision maker or general objective functions; each agent can determine its behavior based on its own strategies. The proposed method was used to design a wastewater reuse network in an industrial park. Although an overall objective function was excluded in the system, the total freshwater use and wastewater discharge were decreased. This suggests that the proposed ABM can generate proper reuse networks. The resulting networks under different scoring methods, which were used as the agents’ environmental behavior indicator, showed different structures including the selection of different center agents. Economic analysis also produced distinctive results, such as the excessive environmental budget. Both the network structures and economic features suggest that the proposed ABM approach can reflect each agent’s behavior to the system without modifying the model structure. As a future research topic, an extended study on data distribution issues will be conducted to elaborate this advantage. The current methodology in the byproduct management system can be expanded by including other practical issues, such as sludge, hazardous residual materials, and additional energy use due to wastewater reuse. Furthermore, how to set the proper price of regenerated water to promote wastewater reuse can be an interesting issue. A wastewater reuse network will attract more attention in the near future because more water shortages are expected. The increasing size of ecoindustrial parks will ensure that the reuse network problem would be more complex, and the corresponding wastewater reuse network will be even more challenging. Advanced methodologies, such as the proposed ABM, are expected to play an ever-increasing role in the future.
Regarding the above result, it is remarkable that the overall wastewater exchange network can be obtained without considering the presence of a central manager who has all the information on the EIPs and has full command of them. This is the main advantage of the ABM versus equation-based modeling. This suggests that the proposed ABM methodology is desirable for handling the diversity and dynamics of the system on account of the differences in the system components. In the equationbased modeling approach, a model was constructed by a central manager with all the information, which makes the process tedious. Another advantage of employing ABM for BPX network design can be addressed in terms of data security issues. A difficult issue in developing EIPs in practice is how to systematically allay the concerns regarding the potential leaking of confidential operational data and related management decisions12 by participating in the EIP. As discussed by Behdani et al.13 and Izquierdo et al.,14 the information and behavior of agents are not perfectly open to the others in an agent-based system. This only allows each agent to make decisions based on only its own interests, avoiding the gathering of supervisory data and control of the system. This works as a conceptual advantage of ABM methodology over other methods in a data security perspective.13,14 The proposed methodology apparently holds the characteristics. As shown in Figure 3, each agent only shares water and wastewater information, hiding its economic data, such as the marginal operation cost, firm size, and environmental preference, which are normally considered as confidential. Therefore, the result according to ABM methodology is more realistic. In an ABM approach, although a single agent cannot provide a solution to the system, a group of agents can deliver an appropriate solution with their limited knowledge on the other agents by interactions.14 Decreases in total water consumption and wastewater generation in the result of the case study highlight the solution-developing capability of the ABM adopted in this research. The proposed agent-based modeling methodology has a conceptual advantage in developing a byproduct reuse network. As claimed by Parunak et al.,15 one of the fundamental differences between equation-based modeling and agent-based modeling is their ways of presenting the system. An equationbased model is formulated with a set of equations that express the relationships among observables (variables). The general formulation of the equation-based mathematical model for a wastewater reuse network included the water demand limit and wastewater information of the processes, or nodes, as well as their relationships in mostly complex MINLP problems. On the other hand, an agent-based model begins, not with equations that relate the observables with one another but with behaviors through which the individuals interact with one another. Therefore, it is fairly common that agent-based models be represented with a relatively simple set of equations.16 This characteristic can also be found in this study. Figure 3 provides a list of equation-like ifthen-else statements describing an agent’s behavior during each step. Realistic wastewater networks can be derived without formulating a MINLP-based superstructure model including hundreds of equations.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This study was supported by the Korea Research Foundation Grant funded by the Korea Government (MOEHRD, Basic Research Promotion Fund) (KRF-2008-313-395 D00178).
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NOMENCLATURE/ABBREVIATIONS Cini,t = process water concentration input into firm i at stage Cout i,t = wastewater concentration from firm i at stage t
t
cu = cost unit Di,j = piping cost between firm i and firm j (cu/h) fci = standardized capital cost of a treatment facility of firm i (cu/h) i,j = firms in an EIP Ii,k = binary variable representing whether firm i selects a treatment facility with size level k k = treatment facility level oci,t = environmental expense of firm i at stage t (cu/h) Pfw = unit freshwater cost (cu/t) Pk = capital cost of a treatment facility with size level k (cu/h) Prw = unit regenerated water cost (cu/t) Ptr = unit wastewater treatment cost (cu/t)
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CONCLUSIONS This paper proposed an agent-based modeling methodology for a wastewater reuse network in an ecoindustrial park. Formulation of the wastewater reuse network was emulated on the basis of an agent-based automated market schema. The network was 10867
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qex i,j,t = regenerated water flow rate from firm i to firm j at stage t (t/h) qex‑final = regenerated water flow rate from final treatment i,t center to firm j at stage t (t/h) qini,t = process water flow rate into firm i at stage t (t/h) qout i,t = wastewater flow rate from firm i at stage t (t/h) ri,j,t = binary variable representing whether firm i sends regenerated water to firm j at stage t rci,t = expenses of firm i at stage t from using regenerated water (cu/h) Si = general characteristic constant of firm i derived from the firm size, R&D ratio, and product type (score 1)i,t = score of firm i at stage t calculated by scoring rule 1 (score 2)i,t = score of firm i at stage t calculated by scoring rule 2 t = system development stage tci,t = total expenses of firm i at stage t (cu/h) ABM = agent-based modeling BPX = byproduct exchange EIP = ecoindustrial park
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(15) Parunak, H. V. D.; Savit, R.; Riolo, R. L. Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide. Proceedings of Multi-Agent Systems and Agent-Based Simulation (MABS’98); 2010. (16) Nagata, T.; Sasaki, H. A Multi-Agent Approach to Power System Restoration. IEEE Trans. Power Syst. 2002, 17 (2), 457.
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