Intelligent Decision Making and System Development for Comprehensive Waste Minimization in the Electroplating Industry

EPA Grant Number: R824732
Title: Intelligent Decision Making and System Development for Comprehensive Waste Minimization in the Electroplating Industry
Investigators: Huang, Yinlun
Institution: Wayne State University
EPA Project Officer: Karn, Barbara
Project Period: September 1, 1995 through September 30, 1997
Project Amount: $100,000
RFA: Technology for a Sustainable Environment (1995) Recipients Lists
Research Category: Sustainability , Pollution Prevention/Sustainable Development


Virtually all manufacturing of precious metal products involves electroplating. In the U.S, there are over 3,000 electroplating plants which utilize more than 100 chemicals to electroplate parts with any one or a combination of over 100 different metallic coatings. This has given rise to the generation of a huge volume of waste streams. While a variety of methodologies and techniques for waste minimization (WM) in this industry have been developed during the past decade, they have not fully permeated the plants. Part of the reason for this is that they have not been well classified and compared in terms of cost and efficiency. More importantly, the implementation of these methodologies and techniques requires various kinds of experience in order to achieve not only WM efficiency, but also the lowest cost and the highest productivity. However, to reach this goal requires a multi-disciplinary effort involving experts from a multitude of fields. Decisions on WM through process analysis, modification, control, and optimization are usually made based only on imprecise, incomplete, and uncertain information. Clearly, standard mathematical approaches may be inapplicable in this task.

In this project, we propose to use a wide spectrum of artificial intelligence techniques to realize comprehensive WM. A sophisticated knowledge base for source reduction, in-process recycling, and source waste (pre)treatment will be developed. Two decision making algorithms are proposed for optimizing the use of both first-principles and heuristic knowledge resided in the knowledge base. The ultimate goal of this project is to develop an intelligent decision support system for deep WM in the plating plants of any size. To the best of the PI's knowledge, this system will be the first of its kind in the nation. The system will perform at an expert level and will be a dependable tool with which the plating industry can significantly improve WM practice with the lowest possible cost. It is expected that the source reduction part of the system will truly help electroplaters evaluate current WM levels and identify new opportunities for further waste reduction in various types of plating processes. The source wastewater treatment with in-process recycling) part of the system will provide decisions on selecting the most efficient and cost-effective (pre)treatment processes. Reductions of about 10 percent of wastewater and sludges will be targeted. This system is expected to be initially tested by selected plants over the project period, and eventually to be introduced into the entire plating industry, with arrangements by the AESF Society and technical help from Hughes Research Laboratories.

Publications and Presentations:

Publications have been submitted on this project: View all 11 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 9 journal articles for this project

Supplemental Keywords:

Computer-aided management decision-support system; electroplating/metal finishing; pollution prevention management system; reverse-drag-out sludge modeling., RFA, Scientific Discipline, Water, Sustainable Industry/Business, Sustainable Environment, Environmental Chemistry, Wastewater, cleaner production/pollution prevention, Technology for Sustainable Environment, Economics and Business, in-process changes, chemical waste, waste minimization, waste reduction, cleaner production, decision making, wastewater reuse, in process recycling, electroplating, green process systems, hazardous waste, integrated process design, metal plating industry, electrochemical techniques, SIC = electroplating, artificial intelligence techniques, pollution prevention, source reduction

Progress and Final Reports:

Final Report