Christine W. Chan

An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management

Abstract This paper reports on an integration of multi-criteria decision analysis (MCDA) and inexact mixed integer linear programming (IMILP) methods to support selection of an optimal landfill site and a waste-flow-allocation pattern such that the total system cost can be minimized. Selection of a landfill site involves both qualitative and quantitative criteria and heuristics. In order to select the best landfill location, it is often necessary to compromise among possibly conflicting tangible and intangible factors. Different multi-objective programming models have been proposed to solve the problem. A weakness with the different multi-objective programming models used to solve the problem is that they are basically mathematical and ignore qualitative and often subjective considerations such as the risk of groundwater pollution as well as other environmental and socio-economic factors which are important in landfill selection. The selection problem also involves a change in allocation pattern of waste-flows required by construction of a new landfill. A waste flow refers to the routine of transferring waste from one location in a city to another. In selection of landfill locations, decision makers need to consider both the potential sites that should be used as well as the allocation pattern of the waste-flow at different periods of time. This paper reports on our findings in applying an integrated IMILP/MCDA approach for solving the solid waste management problem in a prairie city. The five MCDA methods of simple weighted addition, weighted product, co-operative game theory, TOPSIS, and complementary ELECTRE are adopted to evaluate the landfill site alternatives considered in the solid waste management problem, and results from the evaluation process are presented.

Artificial intelligence for monitoring and supervisory control of process systems

Complex processes involve many process variables, and operators faced with the tasks of monitoring, control, and diagnosis of these processes often find it difficult to effectively monitor the process data, analyse current states, detect and diagnose process anomalies, or take appropriate actions to control the processes. The complexity can be rendered more manageable provided important underlying trends or events can be identified based on the operational data (Rengaswamy and Venkatasubramanian, 1992. An Integrated Framework for Process Monitoring, Diagnosis, and Control Using Knowledge-based Systems and Neural Networks. IFAC, Delaware, USA, pp. 49-54.). To assist plant operators, decision support systems that incorporate artificial intelligence (AI) and non-AI technologies have been adopted for the tasks of monitoring, control, and diagnosis. The support systems can be implemented based on the data-driven, analytical, and knowledge-based approach (Chiang et al., 2001. Fault Detection and Diagnosis in Industrial Systems. Springer, London, Great Britain). This paper presents a literature survey on intelligent systems for monitoring, control, and diagnosis of process systems. The main objectives of the survey are first, to introduce the data-driven, analytical, and knowledge-based approaches for developing solutions in intelligent support systems, and secondly, to present research efforts of four research groups that have done extensive work in integrating the three solutions approaches in building intelligent systems for monitoring, control and diagnosis. The four main research groups include the Laboratory of Intelligent Systems in Process Engineering (LISPE) at Massachusetts Institute of Technology, the Laboratory for Intelligent Process Systems (LIPS) at Purdue University, the Intelligent Engineering Laboratory (IEL) at the University of Alberta, and the Department of Chemical Engineering at University of Leeds. The paper also gives some comparison of the integrated approaches, and suggests their strengths and weaknesses.

Discovering rules for water demand prediction: An enhanced rough-set approach☆

Abstract Prediction of consumer demands is a pre-requisite for optimal control of water distribution systems because minimum-cost pumping schedules can be computed if water demands are accurately estimated. This paper presents an enhanced rough-sets method for generating prediction rules from a set of observed data. The proposed method extends upon the standard rough set model by making use of the statistical information inherent in the data to handle incomplete and ambiguous training samples. It also discusses some experimental results from using this method for discovering knowledge on water demand prediction.

USING MULTIPLE CRITERIA DECISION ANALYSIS FOR SUPPORTING DECISIONS OF SOLID WASTE MANAGEMENT

ABSTRACT Design of solid-waste management systems requires consideration of multiple alternative solutions and evaluation criteria because the systems can have complex and conflicting impacts on different stakeholders. Multiple criteria decision analysis (MCDA) has been found to be a fruitful approach to solve this design problem. In this paper, the MCDA approach is applied to solve the landfill selection problem in Regina of Saskatchewan Canada. The systematic approach of MCDA helps decision makers select the most preferable decision and provides the basis of a decision support system. The techniques that are used in this study include: 1) Simple Weighted Addition method, 2) Weighted Product method, 3) TOPSIS, 4) cooperative game theory, and 5) ELECTRE. The results generated with these methods are compared and ranked so that the most preferable solution is identified.

Rule-induction and case-based reasoning: hybrid architectures appear advantageous

Researchers have embraced a variety of machine learning (ML) techniques in their efforts to improve the quality of learning programs. The recent evolution of hybrid architectures for machine learning systems has resulted in several approaches that combine rule induction methods with case-based reasoning techniques to engender performance improvements over more traditional single-representation architectures. We briefly survey several major rule-induction and case-based reasoning ML systems. We then examine some interesting hybrid combinations of these systems and explain their strengths and weaknesses as learning systems. We present a balanced approach to constructing a hybrid architecture, along with arguments in favor of this balance and mechanisms for achieving a proper balance. Finally, we present some initial empirical results from testing our ideas and draw some conclusions based on those results.

A Doctrine of Cognitive Informatics (CI)

Cognitive informatics (CI) is the transdisciplinary enquiry of cognitive and information sciences that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach. CI develops a coherent set of fundamental theories and denotational mathematics, which form the foundation for most information and knowledge based science and engineering disciplines such as computer science, cognitive science, neuropsychology, systems science, cybernetics, software engineering, knowledge engineering, and computational intelligence. This paper reviews the central doctrine of CI and its applications. The theoretical framework of CI is described on the architecture of CI and its denotational mathematic means. A set of theories and formal models of CI is presented in order to explore the natural and computational intelligence. A wide range of applications of CI are described in the areas of cognitive computers, cognitive properties of knowledge, simulations of human cognitive behaviors, cognitive complexity of software, autonomous agent systems, and computational intelligence.

Development of an intelligent decision support system for air pollution control at coal-fired power plants

Abstract Air pollution from power plants is responsible for some of the most pressing environmental problems today. Much research has been done on pollution control for power plants. Contemporary approaches to pollution control often take advantage of computer technology, but research on use of expert systems for power plant management is scarce. In this study an expert system was developed to assist power plant decision makers in selecting an economical and efficient pollution control system that meets new stringent emission standards. The study will also provide the key design parameters for such a system. A fuzzy relation model and a Gaussian dispersion model were integrated into this expert system. Using the fuzzy relation model, the system can quickly select feasible control methods according to the desired removal efficiency. The system will then identify the most cost effective control strategy according to economic considerations provided by users. To assess and ensure effectiveness of the selected method, ambient air quality is simulated using the Gaussian dispersion model and compared with required standards. The developed system was applied to a case study. The results generated show that the system is able to consider the trade-offs between environmental requirement and economic objective, decrease the possibility of pollutant risk, and help the power plant reduce environmental-related capital and operation costs.

Applying knowledge discovery to predict water-supply consumption

Optimizing the control of operations in a municipal water distribution system can reduce electricity costs and realize other economic benefits. However, optimal control requires the ability to precisely predict short-term water demand so that minimum-cost pumping schedules can be prepared. One of the objectives of our project to develop an intelligent system for monitoring and controlling municipal water-supply systems is to ensure optimal control and reduce energy costs. Hence, prediction of water demand is essential. We present an application of a rough-set approach for the automated discovery of rules from a set of data samples for daily water-demand predictions. The database contains 306 training samples, covering information on seven environmental and sociological factors and their corresponding daily volume of distribution flow. The rough-set method generates prediction rules from the observed data, using statistical information that is inherent in the data to handle incomplete and ambiguous training samples. Experimental results indicate that this method provides more precise information than is available through knowledge acquisition from human experts.

An intelligent decision support system for management of petroleum-contaminated sites

Abstract Groundwater and soil contamination resulted from LNAPLs (light nonaqueous phase liquids) spills and leakage in petroleum industry is currently one of the major environmental concerns in North America. Numerous site remediation technologies have been developed and implemented in the last two decades. They are classified as ex-situ and in-situ remediation techniques. One of the problems associated with ex-situ remediation is the cost of operation. In recent years, in-situ techniques have acquired popularity. However, the selection of the optimal techniques is difficult and insufficient expertise in the process may result in large inflation of expenses. This study presents an expert system (ES) for the management of petroleum contaminated sites in which a variety of artificial intelligence (AI) techniques were used to construct a support tool for site remediation decision-making. This paper presents the knowledge engineering processes of knowledge acquisition, conceptual design, and system implementation. The results from some case studies indicate that the expert system can generate cost-effective remediation alternatives to assist decision-makers.

A probabilistic reasoning-based decision support system for selection of remediation technologies for petroleum-contaminated sites

Selection of remediation technologies for petroleum-contaminated sites is difficult given the large number of technologies available and inherent uncertainties involved in the selection process. In this paper, we explore the use of an inexact algorithm for probability reasoning for dealing with the uncertainties involved in the problem. By incorporating domain knowledge as well as the stochastic uncertainty, a probabilistic rule-based decision support system (PDSS) has been developed to support the decision making process. The system has been applied to two case studies, in which the best option of remediation technology can be determined according to calculated probability values. In comparison to deterministic and fuzzy decision support systems, the PDSS can provide a recommendation together with a measure on the reliability or degree to which the recommended decision can be trusted.