Particle filter-based monitoring scheme for simulated bio-ethylene production process

Abstract

ABSTRACTThe use of biomass has been promoted to meet the need for sustainable production of ethylene, which is the most used petroleum-derived in the polymer industry. Ethanol is an alternative feedstock to yield the so-called bio-ethylene through catalytic dehydration in fixed-bed reactors. As the reaction system is strongly endothermic, it is very important to know accurately the reactor temperature to assure the process performance. However, in the industrial context, the process measurements are often uncertain and not all variables can be directly measured online. In this regard, this paper analyses the mathematical modelling and numerical simulation of the ethanol catalytic dehydration and contributes with a monitoring scheme using the Bayesian method known as particle filter. Numerical simulations helped understanding the process behaviour and locating the best position for the temperature sensor in the reactor. From temperature measurements, the proposed inferential tool estimates hidden state variables and unmeasured disturbances, using Sequential Importance Resampling algorithm for the particle filter. The proposal is investigated according to the number of particles and the criterion total error reduction. The results show that the monitoring scheme is able to estimate satisfactorily the process variable profiles, as the temperature and chemical conversion, along the reactor length.