Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

Prediction interval-based modelling and control of nonlinear processes

 Novel computational intelligence-based methods have been investigated to quantify uncertainties prevalent in the operation of chemical plants. A new family of predication interval-based controlling algorithms is proposed and successfully applied to chemical reactors in order to minimise energy consumption and operational cost.

Degradation of phenanthrene and pyrene in soil slurry reactors with immobilized bacteria Zoogloea sp.

The environmental fate of polycyclic aromatic hydrocarbons (PAHs) in soils is motivated by their wide distribution, high persistence, and potentially deleterious effect on human health. Polycyclic aromatic hydrocarbons constitute the largest group of environmental contaminants released in the environment. Therefore, the potential biodegradation of these compounds is of vital importance. A biocarrier suitable for the colonization by micro-organisms for the purpose of purifying soil contaminated by polycyclic aromatic hydrocarbons was developed. The optimized composition of the biocarrier was polyvinyl alcohol (PVA) 10%, sodium alginate (SA) 0.5%, and powdered activated carbon (PAC) 5%. There was no observable cytotoxicity of biocarriers on immobilized cells and a viable cell population of 1.86 × 10¹⁰ g^–1 was maintained for immobilized bacterium. Biocarriers made from chemical methods had a higher biodegradation but lower mechanical strengths. Immobilized bacterium Zoogloea sp. had an ideal capability of biodegradation for phenanthrene and pyrene over a relative wide concentration range. The study results showed that the biodegradation of phenanthrene and pyrene reached 87.0 and 75.4%, respectively, by using the optimal immobilized method of Zoogloea sp. cultivated in a sterilized soil. Immobilized Zoogloea sp. was found to be effective for biodegrading the soil contaminated with phenanthrene and pyrene. Even in "natural" (unsterilized) soil, the biodegradation of phenanthrene and pyrene using immobilized Zoogloea sp. reached 85.0 and 67.1%, respectively, after 168 h of cultivation, more than twice that achieved if the cells were not immobilized on the biocarrier. Therefore, the immobilization technology enhanced the competitive ability of introduced micro-organisms and represents an effective method for the biotreatment of soil contaminated with phenanthrene and pyrene.