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INTEGRATED SIMULATION AND MODELLING APPROACH TO DECISION MAKING AND ENVIRONMENTAL PROTECTION H.H. KHOO1 , T.A. SPEDDING2∗ , L. TOBIN3 and D. TAPLIN4 1

Centre for Engineering and Technology Management, School of Mechanical and Production Engineering, Nanyang Technological University, Singapore 639798; 2 Centre for Manufacturing Systems Engineering, Medway School of Engineering, University of Greenwich, Medway University Campus, Pembroke, Chatham Maritime, Kent, ME 4 4TB, United Kingdom; 3,4 Faculty of Engineering and Physical Systems, Central Queensland University, Rockhampton Campus, Dawson Highway, Rockhampton Qld 4700, Australia (∗ author for correspondence, e-mail: [email protected]; fax: 44 (0) 1634 88 3153) (Received 26 June 2000; accepted 21 March 2001)

Abstract. This paper’s objective is first to test the application of computer simulation and modelling tools in investigating the effects of applying sustainable manufacturing practices in a smelter plant, and second, to prove quantitatively and visually that ‘sustainibility is free’. A simulation model is built to test and capture two different operating polices, ‘Policy 1’ and ‘Policy 2’, of an industrial system. In the case study, the simulation model is designed to reflect the effects of decision making in the activites found in a smelter plant, and to quantify the cost, sustainable and environmental consequences based on the decisions. Apart from providing a means of accurately measuring a system’s performance, the purpose of using simulation tools is also to link the economic factors, such as productivity and total costs, as well as the sustainable factors, such as natural resource and energy consumption of a system. The simulation results prove quantitatively and visually that sustainability is not only ‘free’ but is, in fact, a far better proposition for economic growth in the medium to longer term than traditional forms of management. Key words: computer simulation, decision making, environmental management, process modelling, sustainable manufacturing.

1. Introduction

Despite the increasing usage of the term ‘sustainability’, there are varying interpretations of its meaning and implications, such as how it is related to the concept of progress, economic development and environmental stewardship (BuckinghamHatfield and Evans, 1996; Holmes, 1999; Morgan, 1999). As engineers, environmentalists, policy makers and others have gained experience, many would have recognized a need for practical definitions of ‘sustainable development’ that can guide decisions about materials and manufacturing processes in the productdevelopment stages (Deitz, 1996). This paper is concerned with the notion that sustainability is not only ‘free’ but that it is in fact, commercially a far better proposition for economic growth in the medium to longer term than the traditional forms of purely business management.

Environment, Development and Sustainability 3: 93–108, 2001. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.

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1.1. Definition of sustainable development The definition of sustainable development from Our Common Future (World Commission on Environment and Development, 1987), is “to provide development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs”. This paper sets out to investigate the concept of sustainability in an industrial-based platform. In the investigation, a case study of a generic smelter plant will be presented. The issues of decision making and its impact on the manufacturing performance of a system with regard to resource and energy consumption, emissions, and productivity will be discussed. In the scope of the case study of an industrial system, the definition of sustainable development and manufacturing is presented as: The integration of processes, decision making and the environmental concerns of an active industrial system that seeks to achieve economic growth, without destroying precious resources or the environment.

The processes of a system are the manufacturing activities that generate products and by-products from resources and energy, and the decisions made by the management about its processes will affect the system’s outcome. The environmental concerns of the system are waste and pollution. Economic growth is achieved when the productivity of the system is maintained or increased while its cost is reduced or minimized. These three aspects (processes, decision making and environment) of a system form the basis for the paper’s investigation, and the authors attempt to provide a holistic approach in managing and measuring the system’s performance. In this context, the right method must be adopted in order for a system to be accurately studied, measured and investigated, for the purpose of helping manufacturers improve their environmental and business performance (Waldrip, 1999). A process modelling technique and computer simulation is the solution offered to support the objectives of the investigation. The simulation tools will be used to keep track of the system’s activities and measure its performance in terms of waste and emission levels, resource and energy consumption and production costs. This type of application tools for the purpose of supporting sustainable development is also known as a form of environmental protection by Weaver et al. (2000). 1.2. Paper’s objective The paper’s objective is first to test the application of computer simulation and modelling tools in investigating the effects of applying sustainable manufacturing practices in a smelter plant case study, and second, to prove quantitatively and visually that ‘sustainability is free’. The paper is laid out as follows. The next section introduces the smelter plant as the case study for the paper and the sustainable issues involved. Section 3 presents

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the use of modelling and simulation as tools to investigate the smelter plant’s performance. Section 4 presents the description of the plant’s processes and how decision making affects its performances. Sections 5 and 6 present the simulation results and discussions respectively. Finally, Section 7 concludes the paper.

2. Case study – sustainable and environmental concerns of a smelter

The case study focuses on a generic smelter plant in Queensland, Australia, which produces up to 260 000 tonnes of aluminium annually. The smelter’s carbon plant has the capacity of producing up to 520 tonnes of carbon anodes daily. By-products such as carbon dioxide emissions and solid wastes are an integral part of the carbon anode production processes. The carbon anodes are sent to the reduction plant, where highly energy-intensive operations take place to produce the metal. 2.1. Carbon dioxide Carbon dioxide (CO2 ) is a greenhouse gas, which has, over the past few years, contributed to the rise in global temperature and if present trends continue, the concentration of CO2 in the earth’s atmosphere is expected to double by the middle of next century (Houschild, 1998). As it has been reported that future global emissions may rise, most industrialized countries are seeking new methods to reduce the amount of CO2 emissions from industrial processes and activities (Tegart, 1995; Burritt, 1998; Australian Academy of Technological Sciences and Engineering, 1999; Sheaben, 1999). The CO2 emission levels from the smelter carbon plant are one of the factors that will be investigated in the case study. The smelter plant’s operation is highly energy intensive and the power plant that supplies the electrical energy to the smelter is seen as another agent that accepts coal as input, and in the process of burning coal, also generates excessive release of CO2 . In the simulation study, the monitoring of coal and the release of CO2 is restricted to the smelter plant. However, the cost of generating the power consumed by the plant poses yet another issue that will be taken into consideration in the simulation study. 2.2. Energy, resources and waste The raw materials consumed in the smelter plant’s operations are petroleum coke and pitch in the carbon plant and coal to supply the energy for the reduction plant. The source of petroleum coke is oil and pitch is from coal. ‘Tragedy of the Commons’ (Hardin, 1968) and ‘Limits to Growth’ (Meadows et al., 1972), warn us of the negative consequences of exploitation of the planet’s resources, and

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show that the growth and development of industry is reaching its limits. Included in the factors of these limits, as indicated by their studies, are the excessive use of natural resources and excessive pollution. It was also reported that as far as coal is concerned, there is a possibility that there may be insufficient coal available to meet the needs of future generations (Copeland et al., 1981). Sustainability and energy efficiency are closely linked (Vaitilingam, 1993) and therefore, even for energy sources that may eventually become inexpensive and widely available, increases in energy efficiency will be likely to remain sought after to reduce the resources required (energy, material, etc.) to create and maintain the systems and devices to reduce the associated environmental impacts (Rosen, 1997). It is clear that the earth’s capacity to absorb human and industrial waste has reached critical limits. Industrial waste and huge resource consumption comes from manufacturing processes which are inefficient or not well managed. 2.3. Production cost The manufacturing plant produces sacrificial carbon anodes (made from petroleum coke and pitch) for the aluminium smelting operations. It is a stand-alone facility with a capital replacement value of approximately AUD350 million. As businesses of today start to pay attention to sustainable development and growth (Hart, 1997; Shultz II and Holbrook, 1999), another factor that must be taken into consideration is costs – the cost of operating the smelter plant, cost of material and energy, and finally, the cost incurred by waste products. All these aspects (processes, environment, and cost issues) of the plant are integrated into a holistic simulation study by the use of modelling and simulation tools. Due to the high degree of complexity found in the plant’s operations, the scope of the case study includes the manufacturing operations involved and its direct sustainable concerns mentioned; CO2 emissions, energy and natural resource consumptions, solid wastes and costs.

3. Modelling and simulation of the industrial system

Simulation is a very powerful tool, useful for problems for which a comprehensive mathematical optimization model either cannot be determined, or which cannot be solved analytically. The modelling and simulation of industry and environmental systems have been widely applied for many types of management purposes (Oberhuber et al., 1992; Jørgensen, 1992; Jørgensen et al., 1996; Spedding et al., 1999a). Process Model is a simulation software that combines flowcharting technology and activity-based costing to aid planners and managers assess and analyse their company’s processes, activities and performances. This software by ProModel (ProModel Corporation, 2000) is ideal for looking at a system from a ‘helicopter view’ to enable a comprehensive study of a system’s behavior.


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Figure 1. Industrial system.

The simulation tools, together with the use of activity-based costing or ABC (Spedding and Sun, 1999), provides a means of measuring the economical aspects of a dynamic industrial system. Figure 1 illustrates a simplified industrial system. The system accepts input (resources and energy) and transforms them into products (metal) and by-products (emissions and waste). The purpose of the simulation tools is outlined as follows: • Computer Simulation as a decision making tool Modelling aids in showing how decisions, information and products generated in one department can affect the next (Gielingh et al., 1993). The results of a real system, which can take months or years to be realized can be displayed within a few minutes or seconds. Therefore, the model serves to aid managers, engineers and decision makers alike in the selection of different managing policies. • Measuring System Performance The simulation tools are used to measure and quantify the emission levels of CO2 , solid waste, energy consumption, resource consumption and costs of the smelter plant operations. • Information System By using activity charts that outline the system’s processes, the information flow of the system becomes more transparent and more readily accessible. A comparative study of a simulation model of industry can be found in Spedding et al. (1999b), where a systems approach is employed to investigate the performance of an industrial system by quantifying its environmental impacts.

4. Processes and decision making

The carbon plant manufacturing department produces sacrificial carbon anodes which are used in the reduction plant to produce aluminium metal. The plant, which produces up to 520 tonnes of carbon anodes a day, has a high degree of mechanical complexity and inter-linkage, and although it uses extensive modern

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Figure 2. Simulation model of carbon plant’s manufacturing process.

process control equipment, it requires a high degree of manual intervention. The process flow charts of the carbon plant, as created by the computer simulation tools, are shown in Figure 2. In the carbon plant, raw material consisting of 15%–16% pitch, 60%–70% coke and 14%–25% crushed spent anodes are sized, mixed, vibration-formed, baked and cast to aluminium/cast iron rods to form anodes which are then sent to the Reduction Plant. The key bottleneck and quality constraint is the mixing and vibrationforming portion of the plant. In the plant, high reject rates and machine breakdowns frequently occur due to the non-stop running of the operations. The sources of the rejects are identified in two processes. They are rejected paste from the paste mixer and rejected anodes from the vibration-former. These two types of scrap material incur a loss in time, handling costs and an increase in purchasing more raw material to meet production demands. The management of the plant has the option to: 1. Run the plant as per normal 2. Test out a different operating method In the first option (known as ‘Policy 1’), the manufacturing processes are operating in a non-stop condition to meet production demands. In this case, the output rate of carbon anode production is maximized. However, reject and waste levels are high due to the non-stop operating condition which reduces the time available for maintenance management, impacts safety performance and plant housekeeping. Repairing and maintenance are performed when a machine breaks down, or rather, when the machines have been run to exhaustion. Annually, the operating processes cost up to a few million AUD, which includes the time spent in recovering material losses. This first option consumed high levels of material and high levels of rejected parts, which added to the amount of solid waste which contained petroleum coke and pitch.


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In the second option (known as ‘Policy 2’), the management decides to review and re-schedule the processes. In the second operating set-up, it is decided that the machines will be scheduled to run for a period of time, then shut-off for checking and maintenance, before being scheduled to run again. This pattern allows time for inspection and safety procedures, and tighter monitoring of resources. The constant non-stop production conditions are terminated to also include the time for monitoring of the plant’s reject rates and energy consumed. The adjustment of the system’s processes from its initial state to the second state will be performed in incremental steps; these steps will be captured in the simulation model. 4.1. Measuring parameters Within the carbon plant, the net C value governs the performance of carbon plant production in terms of quality and productivity according to the following equation: Tonnes of Carbon in Reduction Cells −Tonnes of Carbon Butts Returned Net C = Tonnes of Aluminium Produced The net C value is also a measure of how efficiently carbon anodes have been consumed in the reduction cell, a lower value of net carbon demonstrates a better handling of the material and its consumption. A value of about 0.42 typifies a carbon production that is running very efficiently, and a value of about 0.48 shows the opposite. The target for implementing the second policy in the carbon plant is to reduce the total reject rate and the net value by about 3.5% and 4% respectively, within a time period of six months. This is carried out by establishing tighter control and maintenance of the vibro-former, and as well as more sustainable monitoring of the raw materials used in the carbon plant. In the actual system, these parameters are easily measured and quantified. However, in the real system, the sustainable factors that are affected according to these parameters are not easily traced or quantified. Therefore, the simulation model is built to accept the plant’s real parameters as input, and then test the rest of the plant’s performance according to how the initial parameters impact on the others. 4.2. Reduction plant The carbon anodes from the carbon plant are mixed with the alumina in the reduction cells, where reaction takes place in a molten bath to produce aluminium and carbon dioxide. Carbon dioxide is an unavoidable by-product of the aluminium smelting process in the case study. The gas forms when the carbon in the anode combines with the oxygen in aluminium oxide during the smelting process. Among the process parameters that affect net C, the current efficiency is the parameter highlighted for the sake of investigating sustainable issues in the paper.

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Current efficiency is also a parameter that determines how much energy is consumed for producing metal at the reduction stage. In theory, for a reduction process where the current efficiency is 100%, the following reaction takes place: 2 moles of Al2 O3 + 3 moles of C → 4 moles of Al + 3 moles of CO2 By taking into consideration the different molar weights of the elements which are, • • • •

1 mole of Al2 O3 = 2∗ 27 + 3∗ 16 kg = 102 kg 1 mole of C = 12 kg 1 mole of Al = 27 kg 1 mole of CO2 = 12 + 12∗ 16 kg = 44 kg

For a 100% efficient conversion; 204 kg of Al2 O3 + 36 kg C yields 108 kg Al + 132 kg CO2 Therefore, for CE less than 100%, say, 100 − x; 204 kg Al2 O3 + 36 kg C → (100 − x)% of 108 kg Al + 132 kg CO2 Due to back reaction: x%(108 kg Al) + oxygen → x%(51 kg Al2 O3 ) By adding more carbon into the pots: x%(51 kg Al2 O3 + 9 kg C) → x%(27 kg Al + 33 kg CO2 ) Based on this, an additional x% of 33 kg CO2 released for every 108 kg Al produced from the reduction plant. This does not take into account the amount of carbon dioxide released from other factors such as anode burns. Based on the associated CE value, the energy consumed at the cells are expressed as (Grojtheim and Welch, 1980): Energy consumed = (2.980∗ V)/CE kWh/kg Al In applying ‘Policy 2’ to the reduction plant, the plant’s parameters were adjusted to increase and maintain the CE value to a targeted value of 94%. The impact of these decisions will be discussed in the simulation model. 5. The simulation model

Models implemented on computers are described by relations which can be equations, qualitative relationships or logical rules (Rizzoli and Young, 1997). The simulation model is designed to run according to the two separate types of operating conditions based on the operating policies that were discussed in the previous section. The simulation model from Figure 2 is modified to include a feedback loop that automatically adjusts the system’s conditions to employ ‘Policy 2’ into the system in a step-by-step manner. In this manner, the system is monitored slowly to capture the behavior of the initial ‘as-is’ model to the behavior of a modified ‘to-be’ model.


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Figure 3. Simulation model designed to run on ‘Policy 1’ and ‘Policy 2’.

5.1. Impact of decision making It must be noted that the improvements of the reject rates and net C value were performed in the real system – the simulation model is built to reflect these real values (beginning value before improvements were made, and end values after improvement). Therefore, in the model, a linear characteristic is assumed for the feedback loop to demonstrate the plant’s performance. In the real system, some attributes of the plant can be easily monitored and measured; these include levels of production and rejects. Other attributes are ‘invisible’ and not easily quantified; such as emission levels. Therefore the model links the quantifiable objects and the not so obvious or not so quantifiable objects.

6. Simulation results

Model verification is carried out by a ‘walk through’ approach to ensure that the processes in the simulation model reflects the activities of the actual plant. This is to ensure that the model is fit for its intended purpose. After the verification procedures, the model is validated by comparing the results of a one-year production run to the actual plant’s results of the annual rates of carbon, aluminium, and total costs according to normal operating conditions. After model verification and validation, two simulation runs were performed. In the first simulation run, the model operates according to operating Policy 1, and the second, according to operating Policy 2. Each simulation run was for the duration

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of a one-year production period. The results from the carbon plant for comparing the total rejection rate and net C value are displayed in Figures 4 and 5. The total cost of rejects and the ratio of useful product over natural resource consumed are displayed in Figures 6 and 7. The current efficiency value and ratio of carbon dioxide emission over metal produced are displayed in Figures 8 and 9. Finally, the energy consumed for metal production and the cost of energy are displayed in Figures 10 and 11. Figures 4 and 5 show that by applying ‘Policy 2’, the rejection rate is reduced by 3.37%, and the net C value is reduced by 4.12%. These two values were reduced within a time period of six months (4320 h) and then carefully maintained for the rest of the year (total of 8640 h). Both the graphs display a jagged appearance due to

Figure 4. Total rejection rate.

Figure 5. Net C value adjustment.


Figure 6. Total cost of rejects (in millions).

Figure 7. Ratio of useful product over natural resource consumption.

Figure 8. Current efficiency value.

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Figure 9. Ratio of carbon dioxide emissions over metal produced.

Figure 10. Energy consumed.

Figure 11. Cost of energy in reduction plant.


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the random and stochastic data from the plant, which is captured by the simulation model. The ‘noise’ contained in the simulation results are minimal due to the data collected from the plant, which consists of the minimum, maximum and average output values under normal operating conditions. Other types of output data or values caused by unexpected disturbances were unavailable. The change of these two parameters affects the rest of the plant’s performance as follows. Figure 6 demonstrates, as expected, that the cost of rejects decreases according to the decrease of the total reject rate (from Figure 4), and in Figure 7 the ratio of production increases by 7.14%, also due to the plant producing less rejects. The model demonstrates that with more sustainable measures, productivity and quality are also likely to improve. In Figure 8, the current efficiency value is increased to 94%, thereby affecting the ratio of carbon dioxide released for every metal production (shown in Figure 9). This graph is produced according to the calculated emissions discussed in Section 4.2, and does not take into account other emissions such as those from fugitive gases or the burning of the carbon anode in the air. The other parameters affected by the change of current efficiency are an increase in energy efficiency for every metal produced (Figure 10) and the associated cost of energy (Figure 11). The smelter plant produces about 260 000 tonnes of aluminium annually. Based on this amount of metal produced, the possible reduction of the carbon dioxide released from the reduction process and the reduced cost of energy can be considerable. An additional environmental bonus due to better energy efficiency of the reduction process is less burning of coal from the power station due to less power being required. In operating ‘Policy 1’, the output rates of the system is maximized, however, due to frequent machine breakdowns, a large amount of the products were treated as rejects and eventually as waste. In the second operating condition, the system’s output rate is decreased, however, due to less frequent machine breakdowns and reduced reject levels, more of the production output were good products. In this manner, the system’s productivity was well maintained from less material consumed, less energy consumed and less cost incurred. A summary of the percentage improvements for the output results from operating condition 1 to 2 is displayed in Table I.

TABLE I. Summary of simulation results. Model parameters

Annual percentage improvement

Total rejection rate from carbon plant Annual total cost of rejects in millions Net C value Ratio of useful product over natural resource consumed Current efficiency value Ratio of carbon dioxide emissions to total metal produced Energy consumed in kWh per kg metal produced Annual cost of energy

Decrease by 3.37% Decrease by 3.3% Decrease by 4.12% Increase by 7.14% Increase by 1.83% Decrease by 3.5% Decrease by 2.325% Decrease by 2.3%

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7. Discussions

The ‘Limits to Growth’ argument indicates that our way of life is quite unsustainable. Places like Australia are consuming more than their fair share of the world’s resources (Trainer, 1985). In the smelter plant case study, not only has the paper presented the incentives of sustainable manufacturing, but the use of computer simulation as a tool to support the decision making process in the quest for more sustainable practices of an industrial system were also highlighted. This approach has demonstrated the potential of advanced technology in playing a significant role in sustainable manufacturing and development – not because of potential breakthroughs to replace natural resources or air, but because technological advances can facilitate dissemination of information, enhancement of communication and visual verification of making the right decisions (Saemann, 1992; Christensen, 1999). Weaver et al. (2000) sees the next step as applying these tools in other areas such as process integrated innovations and sustainable technologies. Overall, Australia emits around 1.5% of the global greenhouse gases (Australian Academy of Technological Sciences and Engineering, 1997), and the industry faces many challenges in responding rapidly to calls for the reduction of CO2 emissions (Tegart, 1995; 1997). This is another important issue that was addressed in the smelter plant’s simulation study. The simulation results demonstrated that efficient use of energy reduces both the emission levels from the production process, as well as the cost of energy. Apart from cost savings from energy, other cost areas must also be investigated, such as the cost of production. This is due to the increasing pressure of balancing marketing performance with environmental stewardship, which is becoming more important to marketers, consumers and marketing policy scholars (Menon and Menon, 1997; Shultz II and Holbrook, 1999). All these issues are creating new challenges in many companies and soon all managers and decision makers will recognize the need to take into consideration resource preservation and pollution abatement as a precondition for long-term survival, and therefore, as an ingredient for long-term profitability (Gifford, 1997).

8. Conclusion

This paper had two objectives. The first was to test the application of computer simulation and modelling tools in investigating the effects of implementing more sustainable practices in the smelter plant case study. The second was to prove – quantitatively and visually – that ‘sustainability is free’. The simulation and modelling tools have demonstrated its ability to provide a ‘helicopter view’ of the entire system, supported information flow in the decision making process and have accurately measured a system’s output dimensions. The simulation results have quantitatively proven that sustainability (application of ‘Policy 2’) is not only ‘free’


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but is in fact, a far better proposition for economic growth in the medium to longer term than traditional forms of management (application of ‘Policy 1’).

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