A monitoring and diagnostic expert system for carbon dioxide capture

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Abstract

The research objective is to design and construct a knowledge-based decision support system for monitoring, control and diagnosis of the carbon dioxide capture process, which is a complicated task involving manipulation of sixteen components and their operating parameters. Since manipulation of critical parameter values directly impacts performance of the plant and carbon dioxide capture efficiency, it is important to effectively monitor, control and diagnose the process. This paper describes development of a knowledge-based decision support system for the carbon dioxide capture process. The knowledge acquisition process was conducted based on the Inferential Modeling Technique, and the knowledge-based system was implemented with G2. Since the reboiler heat duty is the most significant parameter influencing the carbon dioxide production rate, in the current version, the Carbon Dioxide Capture Monitoring and Diagnostic (CDCMD) system controls the heat duty to maintain the desired carbon dioxide production rate, thereby improving performance of the plant and enhancing efficiency of the carbon dioxide capture process. The CDCMD system provides decision support to the operator in monitoring the process; it can also function as a tutorial system for novice operators.

Introduction

While fossil fuels constitute the primary energy source in the world, its combustion has resulted in emission of large amount of greenhouse gases, and caused global warming. Carbon dioxide (CO2) is by far the most important of the greenhouse gases; it is responsible for about 64% of the enhanced greenhouse effect and global warming (Murlidhar Gupta, 2003). Therefore, with the public’s rising concerns about environmental pollution and climate change, CO2 capture is one of the methods for mitigation of the risks posed by carbon dioxide pollution, and it has been widely used in the natural gas processing and chemical processing industries for over 60 years. The goal of CO2 capture is to capture and remove CO2 from industrial gas streams before they are released into the atmosphere. The current CO2 capture technologies include: absorption by chemical solvent, physical absorption, membrane separation, cryogenic fractionation, and physical adsorption. Since CO2 capture processes require significant amounts of energy, the process of chemical absorption becomes the most widely adopted technology for CO2 capture because it is the most energy efficient.

The chemical absorption process involves using the amine solvent to absorb CO2 from the flue gas; CO2 is subsequently extracted from the amine solvent, which can then be regenerated and reused. Operation of an amine-based CO2 capture system is a complicated task because it involves monitoring and manipulation of sixteen components and over a hundred parameters, including temperatures, flow rates, pressures, and levels of reaction instruments. The monitoring and control of critical parameters is also an important task in operation of the CO2 capture process because it directly impacts plant performance and capture efficiency of CO2. Since the monitoring and control task is complex, it is desirable to build a knowledge-based system that can automatically monitor, control and diagnose the CO2 capture process. In this paper, we present research work conducted with the objective of building a decision support system that can monitor, control and diagnose the CO2 capture process at the International Test Center for CO2 Capture (ITC) located at University of Regina, Saskatchewan, Canada.

The decision support system is called Carbon Dioxide Capture Monitoring and Diagnostic (CDCMD) System. The knowledge base consists of knowledge about the plant and its components, the parameters, and their normal and abnormal operating ranges. Plant components that have abnormal parameter values indicate that abnormal operating conditions have occurred. The knowledge base also consists of the remedial actions that would address these abnormal situations. The CDCMD system can help the operator monitor the operating conditions of the CO2 capture pilot plant by continuously comparing the measured values from sensors with normal or desired values. Deviations from the normal ranges would set off an alarm to advise the operator that a problem has occurred. The KBS can conduct real-time monitoring and diagnosis, as well as suggest remedies for any abnormality detected, thereby improving the performance efficiency of the plant. It can also serve as a tutorial system for novice operators. The knowledge base can be shared and reused, and can contribute to future study of the CO2 capture process. The KBS was implemented with G2, an intelligent object-oriented real-time system developed by Gensym Corp, USA. The paper discusses development of the knowledge-based system and demonstrates use of the system by using scenarios on problems that occur due to abnormal conditions involving heat duty.

The paper is organized as follows: Section 2 presents the background literature relevant to the area of CO2 capture. Section 3 describes the process of development of the knowledge-based decision support system. Section 4 presents implementation of the system on G2 (trademark of Gensym Corp, USA). Application of the system is demonstrated using three case studies in Section 5. Section 6 gives a conclusion and also includes some discussion about future work.

Section snippets

Background literature

Research that focus on improving the effectiveness and efficiency of the CO2 capture system has been ongoing for the past decade. Rao and Rubin (2002) conducted technical, economic and environmental assessment of amine-based CO2 capture technology, and studied the key parameters that affect the performance, cost and environmental acceptability of different technology options. Rao, Rubin, Keith, and Morgan (2006) developed a performance model and a cost model of the CO2 capture plant by defining

Knowledge acquisition

The objective of the knowledge acquisition process is to obtain systematic knowledge about the CO2 capture and extraction process, and identify the knowledge needed for performing monitoring and diagnosis. To develop the knowledge base for the expert system, it is necessary to obtain a thorough understanding of (1) the components in the CO2 processing system, (2) the interconnections among the components, (3) the possible abnormal behaviors of these components, and (4) their possible solutions.

CDCMD system implementation in G2

The CDCMD system has been implemented using an intelligent real-time system development tool called G2 (trademark of Gensym Corp, USA). In the G2 implementation, a workspace is an area of the knowledge base for organizing and displaying the information in the application. The knowledge base of this system consists of four workspaces:

  • (1)

    Object workspace, which consists of definitions of the classes, objects, their attributes and values.

  • (2)

    Procedure workspace, on which the methods are declared. A

Application study case 1

The first sample run presents the scenario in which the system is working normally. CO2 production rate is 0.5 ton/day, and the heat duty is 95,000 BTU/lb-mole CO2, which is over the break point 90,000 BTU/Mole-CO2. The steam pressure is 496.6 kPa, the steam flow rate is 56.8 kg/h, and the reboiler pressure is 35.1 kPa. These values indicate that the components are working under the desirable operating conditions. The user interface that shows this scenario is shown in Fig. 12. The message sent to

Conclusion and future work

The CDCMD system provides decision support to operators of the CO2 capture pilot plant at ITC by maintaining the CO2 production rate at a stable and optimal level. If it is coupled with the SCADA system, the expert system can track the values of the operational parameters. Upon detecting the CO2 production rate to be below the normal range, the system examines the potential causes, analyzes the parameter values received and identifies the cause of the abnormal condition. It then makes a

Acknowledgements

The authors are grateful for the generous support from a NSERC Discovery Grant, and also would like to thank Don Gelowitz for his invaluable contribution during the processes of knowledge acquisition and clarification.

References (7)

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