System Development for Eco-industrial Parks Using Ontological Innovation

Available online at www.sciencedirect.com

ScienceDirect Energy Procedia 105 (2017) 2239 – 2244

The 8th International Conference on Applied Energy – ICAE2016

System development for eco-industrial parks using ontological innovation Li Zhoua, Ming Panb, Janusz J. Sikorskib, Sushant Garuda, Martin J Kleinelanghorstc, I. A. Karimia, Markus Kraftb,c* a

Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585 b Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge, CB2 3RA, United Kingdom c School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459

Abstract Engineering design and system operating comprise highly innovative and knowledge-intensive tasks in the design of an eco-industrial park. Efficient information exchange and communication among distributed parties are very important for a business to succeed. Building a well-structured framework for data/information streamlining and processing is an urgent task in order to achieve further process simulation and optimization in the eco-industrial parks. This paper presents a study of ontological representation for an eco-industrial system, and its deployment on a knowledge-based software platform. The contributions of this work include: Firstly, an ontology model for the relevant chemical process is built relying on the ontological framework provided by OntoCAPE. Secondly, a surrogate modeling method is adopted and implemented for the industrial system. Finally, a Graphical User Interface (GUI), acting as an operating platform, is developed based on the proposed software architectural design. A case study is carried out to demonstrate the chemical process simulation and information query on this platform.

© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

© 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection peer-review of under responsibility of ICAE Peer-reviewand/or under responsibility the scientific committee of the 8th International Conference on Applied Energy. Keywords: Eco-industrial system, Ontology, Chemical process simulation, Information query

* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address: [email protected]

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.637

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1. Introduction Over the past decades, study of eco-industrial parks (EIP) has gained wide popularity in the scientific community. An EIP is defined as a community of neighboring businesses collaborating with each other, seeking enhanced environmental, economic and social performance [1]. An EIP system is a large-scale complex system, comprised of a great number of components from different operation levels, including units, processes, plants and networks [2]. Efficient collaboration between different sectors is the key to EIP success, which requires storing, sharing and processing a large amount of heterogeneous and dispersed data and information. In this scenario, traditional information technology may no longer be able to provide satisfying support. It requires a novel knowledge-based software system, which has two basic components: 1) a knowledge base containing generic domain knowledge and concrete facts specific to the considered case; 2) and an inference engine to process the knowledge and facts stored in the knowledge base, and to generate solutions for the cases at hand. It's obvious that building a valid knowledge base is crucial to the development of an EIP software system. Ontologies are emerging as a useful infrastructure for knowledge representation and sharing. During the past decades, the number of available ontology frameworks has increased rapidly, in particular for the engineering domain. At the earliest stage, EngMath was presented for mathematical modelling in engineering [3], YMIR was reported for representation of engineering design knowledge [4], and PhysSys was developed for modelling generic physical system [5]. Subsequently, ontologies for a wider domain, such as chemical process engineering [6-7], pharmaceutical engineering [8], were proposed and applied. Amongst the reported ontologies, OntoCAPE [9] is the most prominent and well-accepted framework for process engineering. Several of its extensions and applications have been reported [10-12]. Although many ontological frameworks are reported for certain engineering domains and applications, the ontology-based representation and simulation of a large-scale industrial park system have never been achieved. This paper presents an efficient approach for developing a Computer Aided Process Engineering (CAPE) software system for an EIP. OntoCAPE is adopted for knowledge base construction. An efficient surrogate modelling method is developed to describe the performance of complex industrial systems. A software architecture is designed, and a Graphical User Interface (GUI) is developed in order to perform process simulation and information query in the EIP. 2. An ontology-based repository for chemical processes in an EIP

Fig. 1 Representing a chemical process using OntoCAPE.

Li Zhou et al. / Energy Procedia 105 (2017) 2239 – 2244

An EIP system is composed of several industrial production components, such as chemical plants, electrical network, water network, transportation network. To build a complete ontology-based knowledge repository for such a system, all these components must be taken into account. The present work considers one aspect of the EIP system, i.e. the chemical plants. OntoCAPE is utilised to describe the chemical plants. OntoCAPE is characterized as a formal, heavyweight ontology which is represented in OWL modelling language. It was developed based on a sound architecture, facilitating efficient ontology construction, long-term maintenance and its reusability. Different aspects of the CAPE domain (including unit operations, equipment and machinery, materials and their thermophysical properties as well as process behaviour, modelling and simulation) are modelled in OntoCAPE, as shown in Fig. 2. In this work, Materials, process systems (devices and flowsheet), and mathematical models (surrogate models) are considered as the major characteristics used for describing a chemical process. 3. J-Park Simulator: an ontology-based infrastructure for EIP system The primary purpose of developing an ontological representation is to facilitate process design and system operating for an EIP. The ontology is used as the knowledge base for a multi-functional software system, called J-Park Simulator, which is developed as a virtual representation of the EIP system. J-Park Simulator is designed to simulate the industrial activities in the EIP as well as the interactions among them. Fig 2 represents the proposed architecture for J-Park Simulator. OntoCAPE is used to build a knowledge base for the proposed software system. It provides conceptualization rules for representing different aspect of chemical engineering processes, and establishes a guideline for extending it to express other facets of the system, such as electrical network. The EIP system is described in a four-layered structure, from unit level to process level, plant level and industrial level [2]. An advanced software tool (MoDS) is proposed to build the surrogate model for the technical components at each level, which is then stored in a model repository. A graphical user interface (GUI) is designed as the operating platform, which provides a virtual presentation of the EIP system. An applet is designed as the inference engine system to handle all the information managing and data processing. The technical components of the EIP can be associated with their detailed information, mathematical representations (surrogates) through the communication between the GUI and the servlet.

Fig. 2 Proposed architecture for J-Park Simulator.

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4. Case study In this section, a biodiesel plant in an EIP is shown to demonstrate the developed EIP system, which includes process modelling and simulation, and information query. 4.1. Process modeling and simulation The biodiesel plant is first modelled using Aspen Plus. This Aspen Plus model is then simulated to produce substantial sample data for generating a surrogate model of the process using MoDS[15]. To study the process performance, six state variables were chosen as the independent variables. Three objective values were considered, including molar flowrate (F), molar purity (y) and temperature (T) of the final product. The first order polynomial (Eq.1) is employed. Tables 1 and 2 give the detailed surrogate model. ‫ ݕ‬ൌ ‫ ܥ‬൅ σே ௜ୀଵ ‫ܣ‬௜ ‫ݔ‬௜

(1) Table 1 Ranges of input parameters for surrogate modelling of the biodiesel plant. Parameters

LB

UB

x1: Molar flowrate of OIL stream (kmol/hr)

27

33

x2: Temperature of OIL stream (Ԩ)

27

33

x3: Molar flowrate of MEOH stream (kmol/hr)

162

198

x4: Temperature of MEOH stream (Ԩ)

27

33

x5: Molar flowrate of RE-WATER stream

209.835

256.465

x6: Pressure of BOILER (bar)

3.6

4.4

Table 2 Detailed surrogate models of the biodiesel plant. Parameters

F

y

T

C

86.57

0.978

49.1

A1

7.86297

-0.00526

-0.76666

A2

0.0143

0

-0.00138

A3

0.6278

0.00376

-0.01346

A4

0.01095

0

-0.00107

A5

0.01838

0

-0.00192

A6

-0.00559

0

0.00081

0.995

0.986

0.994

R

2

Fig. 3 shows the ArcGIS representation of the biodiesel plant in J-Park Simulator. Each processing unit is associated with its current operating status, including the inlet/outlet stream condition. A user can check the operating status of each unit by clicking on it. The related information will be demonstrated in a popup window. In the pop-up window, the user is allowed to modify and save the information. After modifying the operating parameter, the user can utilize J-Park Simulator to evaluate the new operational status.

Li Zhou et al. / Energy Procedia 105 (2017) 2239 – 2244

(a) Before simulation.

(b) After simulation.

Fig. 3 Illustration for process simulation in J-Park Simulator.

4.2. Information query An ontology-base data repository helps to create structured and machine-readable information. Data and information can be extracted and queried efficiently. Fig. 4 demonstrates the information query for the pumps in the biodiesel plant.

Fig. 4 Illustration for information query in J-Park Simulator.

5. Conclusion This paper presents an efficient approach for developing a Computer Aided Process Engineering (CAPE) software system for the data/information storing, sharing and processing in an EIP. An efficient

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surrogate modelling method has been utilized to accurately describe performance of complex industrial systems. OntoCAPE is employed and extended for knowledge base construction including building the data and model repository for chemical processes. With the proposed software architecture design, process simulation and information query can be achieved through a GUI. 6. Copyright Authors keep full copyright over papers published in Energy Procedia. Acknowledgements This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its CREATE programme. References [1] M. R. Chertow. Uncovering industrial symbiosis. Journal of Industrial Ecology 2007, 11(1):11–30. [2] M. Pan, J. Sikorski, J. Akroyd, S. Mosbach, R. Lau, and M. Kraft. Design technologies for eco-industrial parks: From unit operations to processes, plants and industrial networks. Applied Energy 2016, 175:305–323. [3] T. R. Gruber and G. R. Olsen. An ontology for engineering mathematics. In Fourth International Conference on Principles of Knowledge Representation and Reasoning 1994, pages 258–269. [4] L. Alberts. Ymir: a sharable ontology for the formal representation of engineering design knowledge. Formal Design Methods for Computer-Aided Design 1994, Elsevier/IFIP. [5] W. N. Borst. Construction of engineering ontologies for knowledge sharing and reuse. PhD thesis, Universiteit Twente, Enschede, September 1997. [6] E. Munoz, A. Espu˜na, and L. Puigjaner. Towards an ontological infrastructure for chemical batch process management. Computers & chemical engineering 2010, 34(5): 668–682. [7] W. Marquardt, J. Morbach, A. Wiesner, and A. Yang. OntoCAPE: A Re-Usable Ontology for Chemical Process Engineering. Springer Science & Business Media, 2010. [8] L. Hailemariam and V. Venkatasubramanian. Purdue ontology for pharmaceutical engineering: part i. conceptual framework. Journal of Pharmaceutical Innovation 2010, 5(3):88–99. [9] A. Yang, B. Braunschweig, E. S. Fraga, Z. Guessoum, W. Marquardt, O. Nadjemi, D. Paen, D. Pinol, P. Roux, S. Sama, et al. A multi-agent system to facilitate component-based process modeling and design. Computers & Chemical Engineering 2008, 32(10):2290–2305. [10] S. C. Brandt, J. Morbach, M. Miatidis, M. Theißen, M. Jarke, and W. Marquardt. An ontology-based approach to knowledge management in design processes. Computers & Chemical Engineering 2008, 32(1):320–342. [11] S. Natarajan, K. Ghosh, and R. Srinivasan. An ontology for distributed process supervision of large-scale chemical plants. Computers & Chemical Engineering 2012, 46:124–140. [12] Manual of MoDS (Model Development Suite). URL http://www.cmclinnovations.com/mods.

Biography Dr. Markus Kraft is a Professor in the Department of Chemical Engineering and Biotechnology, University of Cambridge and the director of the Singapore-Cambridge CREATE Research Centre. He has a strong interest in the area of computational modelling and optimisation targeted towards developing carbon abatement and emissions reduction technologies.