DECISION SUPPORT SYSTEMS IN PLASTIC RECYCLING PLANTS

Transcription

1 DECISION SUPPORT SYSTEMS IN PLASTIC RECYCLING PLANTS Carl Hans a, Martin Schnatmeyer a, Klaus-Dieter Thoben a, Igor Sbarski b and John Bishop b a Bremen Institute of Industrial Technology and Applied Work Science at the University of Bremen (BIBA), Hochschulring 20, Bremen, Germany. b Industrial Research Institute Swinburne, PO Box 218, Hawthorn 3122, Australia. For higher recycling quotas of EOL goods, the recycling industry needs economic strategies for optimized reverse supply chains and recycling processes. Especially in the EOL area with a high degree of mixed materials, appropriate systems have to be used for supporting the recycler with decisions during the recycling of products. This paper aims to give an overview on relevant approaches which can help recycling companies to deal with the challenges of this highly dynamic environment. Furthermore, available tools and their application in the plastic recycling area will be presented. Key Words: Automation, Communication systems, Complex Systems, Computer-aided work, Computerintegrated manufacturing, Decision support systems, Interfaces, Manufacturing processes, Tracking applications. 1 INTRODUCTION Plastic Recycling Plants are traditionally related on the Small and Medium Sized Enterprise (SME) sector. Here decisions are usually more related on personal knowledge of the staff than on automated decision support systems. This causes insufficient processes because knowledge gained from single recycling processes cannot be reused for new production lots on customer demands. Another important issue, which makes this sector quite complex, covers the uncertainties related to the emergence of recycling material. Although there are predictable scenarios where for instance residuals from production are continuously delivered to recycling, processes are in more or less stable quantities. However, there are many other scenarios where the available material appears in unpredictable quantities. Furthermore the quality and composition of the incoming material can vary dramatically. In any case the recycling facilities are expected to fulfil the demand of their customers. 1.1 Sector description In general the sector of recycling and especially the recycling of plastics is indicated by various uncertainties. The companies active in this field have to deal with various quantities which are delivered for recycling as well as volatile demands from the customers. These factors make reliable planning strategies very difficult. Another typical problem in the area of plastic recycling is the quality of incoming materials. The deterioration of recycled materials properties as compared to the virgin plastics can be caused by contamination, environmental degradation of the plastics in the course of their application, and thermal and mechanical loads during the recycling process. Examples for such quality indicators are thermal, mechanical or rheological properties of materials such as melting or glass transition temperature, elongation, yield strength, flexural strength, melt flow index etc. [1]. As it is seen from the Table 1 there might be a substantial difference between the properties of virgin and recycled materials that can effect their processing and as a result properties of end-use products. Thus today companies in the recycling sectors aim to react to different situations as flexible as possible. In many cases the proper reaction depends on the experiences of few people. In this context, a decision support system would be desirable which supports the decision maker and furthermore offers new potential for optimisation of the often complex recycling processes. At the end, such systems could help to make the whole process more transparent and thus traceable.

2 Table 1 Some properties of typical recycled polyethylene terephthalate (PET) compared with virgin PET Properties Virgin PET Recycled PET Melting point, C Recrystallisation temperature Tensile modulus, MPa Creep deformation in 0.5 min (at 100 C and 0.05 MPa tensile load), % Viscosity( temperature 270 C, shear rate 25 1/s), Pa s Decision support systems - State-of-the-Art Depending on the application field there are different approaches for decision support systems (DSS). Each of them comes along with its own strengths and weaknesses and thus is applicable only for certain areas. However, keeping in mind the diversity of the decision problem, it is absolutely necessary to be aware of the main problem areas related to recycling. In this paper three problem areas in the recycling process are considered as the main ones: 1. Uncertainties regarding material quantities and qualities 2. Material treatment 3. Production, warehousing and logistics For all of these areas particular approaches can be identified supporting the decision maker. The most important ones are listed below together with a reference to the problem area: Simulation (1, 3) Formal (mathematical) approaches (3) Expert Systems, Neural Networks (2, 3) Benchmarking (2, 3) Specific methodologies (2) The list above shows that none of the methods is able to cover all of the problem domains. Thus a combination of several is desirable. Although all of the concepts mentioned are well defined and have been used for decision support in various areas, none of them are available in the form of user friendly tools. In particular, formal approaches like mathematical programming (e.g. integer / mix-integer programming) are used for problems related to production warehousing and logistics. However, significant knowledge is required in order to apply them which makes them unusable for domain experts as decision makers usually are. Methods from artificial intelligence, like neural networks or expert systems, usually suffer in this context from the necessity to train them or to specify complex knowledge bases. Their application is therefore often limited to certain scenarios. An extension requires significant effort. Benchmarking approaches require a significant number of cases / scenarios in order to be useful. The main problem in this context covers the willingness to deliver internal data. Thus the most promising candidates for effective decision support systems in the field of plastics recycling are simulationbased approaches as well as certain methodologies which have been specified for particular problem domains together with accompanying tools in order to simplify their application for domain experts. In addition to the requirements regarding applicability as well as user-friendliness previously mentioned, an important issue in this context covers the integration of these approaches / environments into the value creation chain. Existing solutions will be shortly presented in the following chapter. 1.3 What can innovative technology offer? An interesting question in this context covers the application of innovative concepts like the concept of Product Embedded Information Devices (PEID) - which is considered in the PROMISE project (see section 3). 2

3 Figure 1. Product Information Model Enabled by Auto-ID [2]. In fact such technologies offer considerable potential to be exploited for decision support. As shown in Figure 1, the active behaviour of product entities could be utilised in order to develop completely new environments or enhance existing ones by making them more responsive due to the pervasive availability of product related information anytime and nearly everywhere. In particular available model-based solutions (like simulation) can benefit from such technologies because their main deficiency is in the timeliness of the information kept by the underlying model. Appropriate concepts exploiting the available information can reduce the costs and time for solution finding dramatically. 2 DSS INTEGRATION With respect to the integration of the simulation-based DSS ONE (see section 3) into the current scenario, the main goal is to provide real-time data for incoming materials and goods on the basis of the required specifications for PEIDs and RFIDs within the scenario. Real-time data will be provided for all of the relevant process steps within the scenario for the support of back-end systems such as Warehouse Management Systems (WMS) and Production Planning Systems (PPS). Afterwards the planning system will be able to simulate basic processes like purchasing and sales planning taking into account the availability of materials, maintenance costs, etc. on the basis of real-time in-formation provided by PEIDs. All the real-time data information logistics which are offered by the PROMISE solution will be handled by the middleware. That means, the PROMISE middle-ware needs to be capable of managing the data flows among the recycling machines, transport systems, and control systems. Furthermore, it must provide the links between the software embedded in the PEIDs, PEID readers and back-end systems and should be based on platforms used in PROMISE and is compatible with other application scenario implementations. In order to handle the potentially vast amount of product information generated within the scenario, the middleware must furthermore provide suitable filtering mechanisms for the data flow between the system components and especially toward back-end systems. To that end, it should include mechanisms such as plausibility checking, duplicate data avoidance, data persistence monitoring, etc. In that context, depending on the design and specification of both the middleware and the DSS, it is possible for the middleware to incorporate DSS functionality where suitable. Last, but not least, the middleware is expected to integrate suitable security mechanisms for protection of personal and process relevant data. For the integration of the ONE tool with the PROMISE environment a pure data-based interface should be considered (at first) for the first step. Although more or less manual, this approach would allow the connection for less costs and efforts than for a more direct coupling which can be looked at a second step. Thus the exchange of data will be conducted via file creation, transmission and import into the ONE system. Especially for file creation internal mechanisms provided by the PROMISE platform will be applied in order to generate the right format and content. The import process is shown by the following screen shot (Figure 4). For each variable the system allows the specification of a constant value, according to a certain distribution function or in the form of a file where the data will be taken from. The example below shows the specification of a certain user demand. 3

4 Figure 2. Data import in ONE. After the import of the data representing customer demand for a certain period in time the DSS can support purchase planning as well as sales in determining available materials and sales planning. In addition, logistics solutions (distribution networks, etc.) can be planned and evaluated before being implemented in reality with the simulation component. Thanks to this component it is also possible to consider uncertainties regarding customer demand or the quantity of material available for recycling. Additionally, process related risks can be investigated, e.g. regarding the impact of delays of deliveries due to accidents or the technical breakdowns during the recycling process. 3 RESEARCH BACKGROUND The described integration concept is based on different research projects, which will be shortly described in this chapter. 3.1 Product Lifecycle Management and Information using Smart Embedded Systems (PROMISE) PROMISE is an international research project in the IMS (Intelligent Manufacturing Systems) program with industry and academia consortia in Australia, Europe, Japan and the United States (see Figure 3). The project concerns the whole information flow from Design, Production, Use-Service-Maintenance or MOL (Middle-of-Life) and Retirement or, as most commonly is called, EOL (End-of-Life). The European part (funded by the European Commission) will develop Information Models, Architecture and Implementation concepts for the PEID technology. This includes also models for decision making. 4

5 Requirements and Specifications CH EU USA AUS Information Modeling, Architecture, Implementation DFX Logistics/ Decision making MOL EOL PEID technology Prototypes and Demonstrators Japan Modeling and Simulation Figure 3. IMS PROMISE regional cooperation [3]. 3.2 An End-of-Life of Product Systems (AEOLOS) The methodology and toolset developed in AEOLOS will provide support to decision-makers both within the public sector and in industry. Stakeholders addressed by the AEOLOS are: Society o o o Manufacturers Remanufactures Reclaimers Recyclers Incinerator operators Landfill operators Title owners of product and Distributors. as a heterogeneous group, and including future generations as particular communities or interest groups as represented by government at all levels Decision-makers in all of these areas formulating policy at local authority, regional, national and EU levels will be supported with the means by which to determine, analyse and evaluate the most appropriate EOL treatment option from an environmental, economic and social perspective for their geographical areas of concern. Similarly, resource recovery/recycling companies and original equipment manufacturers (OEMs) will be provided with decision support as to the choice of EOL treatment options, based on a set of environ-mental, economic and social criteria. The solution is progressing with a methodology and a web-based toolset supporting it s application (see Figure 4 below). 5

6 Figure 4. AEOLOS web-based toolset [4]. 3.3 Optimisation methodologies for Networked Enterprises (ONE) The project ONE (Optimisation methodologies for Networked Enterprises) developed an integrated decision support tool for the evaluation and optimisation of existing as well as the planning of new supply chain networks. In this context not only monetary aspects were considered but also environmental and social impacts, along with certain network specifications. Furthermore, the tool supports risk assessment which means the analysis of the robustness of supply chain networks against unpredictable events such as accidents, bankruptcies or natural disasters. As another important issue, the tool is able to deal with uncertainties like volatile customer demand. The ONE-System encompasses the following components: A network modelling module providing user friendly support for graphical, interactive development of supply chain networks. A statistical data miner allowing the analysis of existing data material and the extraction of information (e.g. in the form of specific probability distributions for customer demand) in order to make network descriptions more realistic. A set of optimisation tools each of which supports the optimisation of certain aspects within a network model. In order to prove the impacts of changes or network configurations proposed by the optimisation module, ONE includes a simulation engine for the emulation of a network model. Especially the consideration of uncertainties as well as risk together with the combination of optimisation methodologies and simulation makes the ONE system an interesting approach for decision support while dealing with today s highly dynamic supply chain network planning and optimisation. The combination of these functionalities make ONE a prime candidate for integration with new technologies like the one which is proposed by the PROMISE project. 4 CONCLUSION The paper gives an overview about the specific requirements in the field of plastics recycling. Furthermore, different approaches for decision support within this area were discussed. It was shown that different concepts come along with their own strengths and weaknesses. Due to the requirements regarding applicability, user-friendliness and integration into the value creation process most of these concepts are not suitable. On the other hand, even promising environments namely simulation as well as specific methodologies and the accompanying toolsets - are not able to cover all end-users needs on their own. Thus a combination of both concepts would be desirable. Furthermore it was detected that the most challenging requirement clearly concerns the integration of DSS tools into real life. In this context the paper gave an overview about a possible approach demonstrated along the simulation-based DSS tool ONE. In addition, the potential benefits coming along with innovative concepts like the one which is considered by the PROMISE project were addressed. In this context major improvements regarding the accuracy of the results from available DSS solutions can be expected due to the improvement of the underlying model (information). 6

7 The PEID concept perfectly fits to the concept of GRID computing for the globally distributed topology comprising product entities and the associated information. Formerly more utilized in the field of high performance computing, GRID computing is nowadays investigated regarding it s suitability for other application fields, e.g. distributed enterprise networks. However, the integration of PEID and GRIDs can be an interesting approach, especially for the development of a new generation of decision support systems which are able to consider global environments for solution finding. 5 REFERENCES 1 Bishop, J., I. Sbarski and E. Kosior (2004) Reuse of Post Consumer Paper and Plastic Materials. Proceedings International IMS Forum 2004, S Biassono (Milano). 2 Mcfarlane, D. (2003) The impact of Product Identity on Industrial Control Part 1: See More, Do More. White Paper CAM-AUTOID-WH-012, Auto-ID Center, MIT. Available at 3 PROMISE (2005) Product Lifecycle Management and Information using Smart Embedded Systems. (07/11/2005). 4 Goggin, K. and Schnatmeyer, M. (2002) AEOLOS - An-End-Of-Life of Product Systems. Proceedings EUROSUSTAIN 2002 Implementing the Integrated Product Policy, CD-ROM. Rhodes, Greece. Acknowledgments The authors wish to acknowledge the European Commission, AusIndustry in Australia and the PROMISE consortium for their support. 7

8 6 BIOGRAPHY OF AUTHORS John Bishop is the Business Manager of the Industrial Research Institute Swinburne (IRIS), which is one of Australia s leading technology and industrial R&D organisations. John has a B.Sc. in Chemistry and a M.App.Sc in Environmental Engineering, both from the University of Melbourne. Prior to joining IRIS 8 years ago, he spent 2 years in the fibre optic telecommunications industry, 8 years with GBC Scientific Equipment as a Product Manager and European Marketing Manager, and 5 years working at the University of Melbourne. Over the past 15 years he has been involved in developing products from concept through to research & development, prototyping, production, marketing and finally sales, where over 95% of production was sold to over 60 countries. He has lived in Germany and the UK, and has local first hand experience of industry practice in over 30 countries, mainly in Europe, SE Asia, North America and the Middle East. Igor Sbarski is a research leader at Swinburne University of Technology (Melbourne) since He received Ph.D degree at Royal Melbourne Insitute of Technology and equivalent degree at Oil and Gas Academy (Moscow). He has been doing research on mechanical and rheological properties and processing of plastics for twenty years. His special interest is recycling of plastics and composite materials where he supervises a number of Ph.D and Master by Research projects. Klaus Dieter Thoben holds a professorship on Application of Information and Communication Technologies in Manufacturing at the University of Bremen. In addition he is member of the board of directors of BIBA (Bremen Institute of Industrial Technology and Applied Work Science at the University of Bremen) and head of IKAP (Applied Information and Communication Technology for Production) at BIBA. His special interests are organizational issues and applications of information and communication technologies in the extended enterprise, concurrent engineering and customer driven manufacturing. Carl Hans received his MSc in Computer Science at the University of Bremen. He started as a research scientist at BIBA in In his research he is concentrating on simulation and optimisation especially in the area of supply chains and logistics networks. In this context he is working on European as well as national and regional projects. Another field of activity / experience of Mr. Hans covers the software development process. This comprises all the phases beginning with requirements analysis over development until testing and documentation. 8

9 Martin Schnatmeyer received his diploma in production technology at the University of Bremen. Afterwards he worked as an engineer for a plastic recycler and compounder. Since 2000 he is a research scientist at BIBA and works in the IT area with special references to environmental aspects, waste treatment and reverse logistics. His research work is related on European, national and industrial projects with the above mentioned aspects. Currently he is involved in two research projects dealing with RFID / embedded systems in combination with the reverse logistics and EOL sector (OPAK, national funded and PROMISE IST/NMP funded). 9