Intelligent decision support for waste minimization in electroplating plants

Abstract Wastewater, spent solvent, spent process solutions, and sludge are the major waste streams generated in large volumes daily in electroplating plants. These waste streams can be significantly minimized through process modifiction and opertion improvement. In this endeavor, extensive knowledge covering various disciplines is required, which makes problem-solving extremely difficult. Moreover, available process data pertaining to waste minimization (WM) is usually inprecise, incomplete, and uncertain due to the lack of sensors, the difficulty of measurement, and process variations. These hinder the use of rigorous mathematical approaches in formulating WM problems. In the present work, an intelligent decision support system, namely WMEP-Advisor, is developed by resorting to artificial intelligence and fuzzy logic. This system is capable of performing detailed process analysis on waste-generation mechanisms, evaluating WM practice for an individual process unit or an entire plating process, identifying WM opportunities, and providing adequate decision support to process and environmental engineers for process modification and operational change. The tool can be used for either on-site WM or off-line personnel training.