Data-Driven Optimal Operation of the Industrial Methanol to Olefin Process Based on Relevance Vector Machine

Abstract Methanol to olefin (MTO) technology provides the opportunity to produce olefins from nonpetroleum sources such as coal, biomass and natural gas. More than 20 commercial MTO plants have been put into operation. Till now, contributions on optimal operation of industrial MTO plants from a Process Systems Engineering perspective are rare. Based on relevance vector machine (RVM), a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets. RVM correlates the yield distribution prediction of main products and the operation conditions. These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant. Nondominated sorting genetic algorithm II is used to solve the optimization problem. Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework. Since RVM does provide the distribution prediction instead of point estimation, the established model is expected to provide guidance for actual production operations under uncertainty.