Gordon Huang

A fuzzy decision aid model for environmental performance assessment in waste recycling

This study introduces a methodological framework for environmental performance assessment of waste recycling programs. We develop two categories of indicators: the efficiency indicators that compare the environmental achievements of a program with the required expenditures (benefits per unit cost), and the effectiveness indicators that compare the environmental benefits of a program with the amount of generated wastes (benefits per unit waste). Aggregation of these indicators, in relation with their associated criticalities, will give us a number of environmental performance indices to represent the status of the environmental performance. This score-based assessment has two major advantages: it takes complex scientific information and synthesizes it in a way that makes it easily understandable for non-experts while in comparison with other environmental performance assessment methods it is not computationally intensive. In this aggregation, the importance values (criticalities) are often expert based uncertain judgments, which are defined according to the objective of performance assessment. Therefore, a fuzzy multiple attribute analysis can be employed to express these judgments by fuzzy sets and to formulate the weighted aggregation process. For case study, we have investigated the environmental performance of the provincial beverage container recycling programs in Canada, which illustrates the applicability of the proposed methodology.

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.

Emerging usage of electrocoagulation technology for oil removal from wastewater: A review

Electrocoagulation is a simple and efficient treatment method involving the electrodissolution of sacrificial anodes and formation of hydroxo-metal products as coagulants, while the simultaneous production of hydrogen at the cathode facilitates the pollutant removal by flotation. Oil is one of the most important hydrocarbon products in the modern world. It can cause environmental pollution during various stages of production, transportation, refining and use. Electrocoagulation treatment is particularly effective for destabilization of oil-in-water emulsions by neutralizing charges and bonding oil pollutants to generated flocs and hydrogen bubbles. The development of electrocoagulation technologies provided a promising alternative for oil removal from wastewater. This paper presents a review of emerging electrochemical technologies used for treating oil-containing wastewater. It includes a brief description of the oily wastewater origin and characteristics. The treatment processes developed so far for oily wastewater and the electrocoagulation mechanisms are also introduced. This paper summarizes the current applications of electrocoagulation for oil removal from wastewater. The factors that influence the electrocoagulation treatment efficiencies as well as the process optimization and modeling studies are discussed. The state-of-the-art and development trends of electrocoagulation process for oil removal are further introduced.

Effects of fulvic acid concentration and origin on photodegradation of polycyclic aromatic hydrocarbons in aqueous solution: importance of active oxygen.

With an Xe arc lamp house as simulated sunlight, the influences of fulvic acid (FA) concentration and origins on photodegradation of acenaphthene, fluorine, phenanthrene, fluoranthene and pyrene in aqueous solution have been studied. Similar effects of FAs, collected from five places around China, on polycyclic aromatic hydrocarbon (PAH) photodegradation have been observed. Active oxygen was of significance in PAH photodegradation with the presence of FAs. For systems with 1.25 mg L(-1) FAs, the contributions of 8*OH to PAH photodegradation rates were from 33% to 69%. FAs had two opposite effects, i.e., stimulating the generation of active oxygen and advancing PAH photodegradation; competing with PAHs for energy and photons and restraining PAH photodegradation. Generally, photodegradation rates of the 5 PAHs decreased with the increase of FAs concentration; except fluoranthene and pyrene were advanced in solutions with low FA concentration. The influences of FA concentration on PAH photodegradation were more significant than FA origin.

AN INDEPENDENT VARIABLE CONTROLLED GREY FUZZY LINEAR PROGRAMMING APPROACH FOR WASTE FLOW ALLOCATION PLANNING

This paper proposes an independent variable controlled grey fuzzy linear programming (IVC-GFLP) approach to address uncertainty in optimization processes. The IVC-GFLP method improves upon established grey linear programming (GLP) and ordinary grey fuzzy linear programming (GFLP) methods by introducing independent control variables into model formulations. These variables enable the model to address the independent characteristics of constraint uncertainty well. In this paper, the IVC-GFLP approach is applied to a hypothetical case study of municipal solid waste management. Included comparisons between the IVC-GFLP and GLP/GFLP solutions indicate that the IVC-GFLP approach can provide more realistic and applicable solutions than its counterparts.

A dual-interval vertex analysis method and its application to environmental decision making under uncertainty

In this study, a dual-interval vertex analysis (DIVA) method is developed, through incorporating the vertex method within an interval-parameter programming framework. The developed DIVA method can tackle uncertainties presented as dual intervals that exist in the objective function and the left- and right-hand sides of the modeling constraints. An interactive algorithm and a vertex analysis approach are proposed for solving the DIVA model. Solutions under an associated [alpha]-cut level can be generated by solving a series of deterministic submodels. They can help quantify relationships between the objective function value and the membership grade, which is meaningful for supporting in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system optimality and reliability. A management problem in terms of regional air pollution control is studied to illustrate applicability of the proposed approach. The results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers to identify desired pollution-abatement strategies with minimized costs and maximized environmental efficiencies.

Enhanced-interval linear programming

An enhanced-interval linear programming (EILP) model and its solution algorithm have been developed that incorporate enhanced-interval uncertainty (e.g., A±, B± and C±) in a linear optimization framework. As a new extension of linear programming, the EILP model has the following advantages. Its solution space is absolutely feasible compared to that of interval linear programming (ILP), which helps to achieve insight into the expected-value-oriented trade-off between system benefits and risks of constraint violations. The degree of uncertainty of its enhanced-interval objective function (EIOF) would be lower than that of ILP model when the solution space is absolutely feasible, and the EIOFs expected value could be used as a criterion for generating the appropriate alternatives, which help decision-makers obtain non-extreme decisions. Moreover, because it can be decomposed into two submodels, EILPs computational requirement is lower than that of stochastic and fuzzy LP models. The results of a numeric example further indicated the feasibility and effectiveness of EILP model. In addition, EI nonlinear programming models, hybrid stochastic or fuzzy EILP models as well as risk-based trade-off analysis for EI uncertainty within decision process can be further developed to improve its applicability.

Fuzzy logic control for a petroleum separation process

This paper presents an automated fuzzy logic controller (FLC) that can be used to improve oil quality produced from a separation process. Automated FLCs based on fuzzy logic are proven to be effective solutions for complex, nonlinear, or uncertain systems such as oil battery configurations. A crucial variable in quality control of crude oil separation is the gas-liquid ratio (GLR), and adjustments on this variable directly control the quality of oil produced during the separation process. The FLC controls the pressure and liquid levels within an oil separator and both variables are associated with the GLR. The objective of this study is to design and develop an automated fuzzy logic process controller for an oil battery based on the experience of petroleum engineers, who monitor and control the equipment of the production and separation facility. This paper discusses design and implementation of the FLC system including data fuzzification, knowledge base creation, and the defuzzification processes. This FLC system has been tested at a real-world petroleum site in Canada. The results indicated that the developed artificial intelligence-aided control system could improve output oil quality by reducing the difference between actual and desired GLR.

Model predictive control for in situ bioremediation system

Abstract This study describes development of a dynamic predictive control system for the in situ bioremediation process. This automated control system not only balances the complex physical, chemical, and biological processes involved in the remediation process, it also minimizes overall cost of the entire remediation process. The control system is on-line, dynamic, and built based on a predictive model. It includes an optimization tool that consists of a simulation model and an optimization function. The numerical simulation model describes the fate and transport of the subsurface contaminants, while the optimization function is a constrained nonlinear function that has been implemented using genetic algorithms. The system was applied to a lab experiment, and tested with data from a real world site. The results indicated that the dynamic, simulation model-based control system can generate an appropriate control strategy and adjust control actions dynamically. This helps to improve efficiencies of the in situ bioremediation process at petroleum-contaminated groundwater systems.

A fuzzy expert system for site characterization

Abstract Remediation selection expert system (RSES) is a rule-based expert system which is used for the selection of remediation techniques for petroleum contaminated sites. In this paper, we describe a fuzzy logic-based subsystem: site characterization subsystem (SCSS). It is an enhancement of the RSES, which is used to analyse the hydraulic properties of contaminated sites. This paper focuses on an explanation on how to apply fuzzy set theory for identification of soil types and hydraulic properties of a contaminated site. To illustrate application of fuzzy set theory to the problem, two sample cases are presented in detail.