A Classification Framework for Process Operation Optimization and its Application in a Triazophos Plant

Knowledge-based operation optimization methods may suffer from difficulties in modeling the chemical processes and solving the mathematical equations. In this paper, a data-based classifica-tion method for operation optimization is introduced. In contrast with other fields, chemical proc-ess is characterized by time delay and interaction between upstream and downstream units. By rebuilding historical data and constructing a group of multiple classifiers, both of the characterized problems are overcome. Some qualitative operational advice may extract from the group of multi-ple classifiers. As a result, the operation of chemical processes may achieve to a reachable optimal state using rolling optimization strategy by updating the classifiers. In addition, some special data-preprocessing techniques are considered to improve the efficiency of the classification. This classification framework customized for chemical process helps a Triazophos plant to improve the productivity of Triazophos from 93.3 % to 95.8 % after implementation of the proposed method for more than one year