Thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Technology: Department of Chemical Engineering, Durban University of Technology, 2007. / The metal finishing industry has been rated among the most polluting industries worldwide. This industry has traditionally been responsible for the release of heavy metals such as chrome, nickel, tin, copper etc into the environment. The application of cleaner production systems to a range of industries, including the metal finishing industry has provided significant financial and environmental benefits. An example of a successful application cleaner production in the metal finishing industry is the reduction in the typical water consumption from 400 1/m² to less than 10 1/m² of plated product. The successful application of cleaner production to the mental finishing industry has encountered many barriers. These barriers include the need for a highly skilled cleaner production auditor and the need for rigorous plant data to effectively quantify the cleaner production potential of the company under consideration. This study focuses on providing an alternate user-friendly audit system for the implementation of cleaner production in the mental finishing industry. The audit system proposed eliminates the need for the need for both a technical auditor and rigid plant data. The proposed system functions solely on plant operator inputs. The operator’s knowledge is harnessed and used to conduct an efficient and effective cleaner production audit. The research is based on expert knowledge, which was gained by conducting audits on some 25 companies using traditional auditing tools. This company audits were used to construct a database of data that was used in the verification of the models developed in this study. The audit is separated into different focus components. The first system developed was based on fuzzy logic multi variable decision-making. For this system the plant was categorized into different sections and appropriate fuzzy ratings were allocated based on experience. Once the allocations were completed multi variable decision analysis was used to determine the individual variable impact. The output was compared and regressed to the database equivalent. Operator inputs can then be used to determine the individual category outputs for the cleaner for the production rating for the company under consideration. The second part of this study entails the development of mathematical models for the quantification of chemical and water consumptions. This was based on the present and ideal (cleaner production) plant configuration. Cleaner production operations are compared to present operations and potential savings quantified. Mathematical models were developed based on pilot scale experiments for the acid, degreaser and zinc plating process. The pilot experiments were carried out on a PLC controlled pilot plant. These models were developed form factorial experimentation on the variables of each of the plating processes. The models developed aid in the prediction of the relevant optimum consumptions. The key challenge in traditional evaluation systems has been the quantification of the plant production. The most effective measure of production is by means of the surface area plated. In this study a novel approach using the modeled acid consumption is proposed. It was assumed that the operator inputs for the above models would not be precise. The models developed allowed for input variations. These variations were incorporated into the model using the Monte Carlo technique. The entire cleaner production evaluation system proposed is based on an operator questionnaire, which is completed in visual basic. The mathematical model was incorporated into the visual basic model. For the purpose of model verification the mathematical models were programmed and tested using the engineering mathematical software, Mat Lab. The combined fuzzy logic and mathematical models prove to be a highly effective means of completing the cleaner production evaluation in minimal time and with minimal resources. A comparative case study was conducted at a local metal finishing company. The case study compares the input requirements and outputs from the traditional systems with the system proposed in this study. The traditional model requires 245 inputs whilst the model proposed in this study is based on 56 inputs. The data requirements for the model proposed in this study is obtained from a plant operator in less than one hour whilst previous models required high level expertise over a period of up to two weeks. The quality of outputs from the model proposed is found to be very comparable to previous models. The model is actually found to be superior to previous models with regards predicting operational variations, water usages, chemical usages and bath chemical evolution. The research has highlighted the potential to apply fuzzy-mathematical hybrid systems for cleaner production evaluation. The two limitations of the research were found to be the usage of a linear experimental design for model development and the availability of Mat Lab software for future application. These issues can be addressed as future work. It is recommended that a non-linear model be developed for the individual processes so as to obtain more detailed process models. / National Research Foundation, Water Research Commission and Durban University of Technology