Increasing public pressure, more stringent regulations, and escalating waste treatment and disposal costs have motivated the chemical industries to implement waste minimization at the source rather than to rely on end-of-pipe treatment. Waste minimization analysis is time-consuming, expensive, laborious, and knowledge-intensive. The objectives of this two-part paper are (1) to develop a systematic, methodology to guide a nonexpert with the technical aspects of waste minimization and (2) to implement an intelligent system that can automatically perform a waste minimization analysis of a chemical process plant. In part 1, a systematic methodology for waste minimization analysis is presented. An intelligent decision support system that implements this methodology is presented in part 2. The proposed methodology comprises three fundamental elements: process graph (P graph) and cause-and-effect and functional knowledge. The P graph is a directed bipartite graph capable of abstracting the flow of materials in a process. An analysis based on the P graph provides a framework for diagnosing the origins of waste in the process and for deriving top-level waste minimization alternatives. These top-level alternatives can then be distilled further by using cause-and-effect and functional knowledge to obtain detailed alternatives. The application of the methodology is illustrated using an industrial case study.