(550f) Reduced Data Techniques for Cleaner Production Evaluation for Surface Treatment Plants

The metal finishing industry has been known to consume chemicals and heavy metals that require optimum usage so as to reduce release into the environment. Traditional cleaner production evaluation systems have proven difficult to complete as data, skills and time availability has been a challenge for these small to medium size enterprises.

An artificial intelligence/mathematical hybrid model is proposed for cleaner production evaluation in the metal finishing sector. Traditional data intensive models can be replaced with hybrid models requiring reduced data. This papers covers the development of a Fuzzy logic and mathematical model that reduces the data requirements for cleaner production evaluations at a plating facilities. The model comprises of a questionnaire requiring operator level inputs. The model receives the operator data and conducts a cleaner production evaluation that is comparable to traditional data intense models. The cleaner production assessment can be conducted in less than a few hours as compared to rigid studies that are conducted over days/weeks. A case study of the model application is presented.