Automation of data processing of the results of the chemical experiment on modeling the production of synthetic rubber using Microsoft Excel

The article presents an approach to the processing of chemical experiment’s data using a Microsoft Excel software. Instead of storing the experiment data in text files, it is proposed to use a Microsoft Excel file of a certain structure. A macro has been developed to automate the process of transferring data from text files to a common file. The described approach can be applied when solving problems accompanied by storing a large amount of statistical data, which can be obtained as a result of natural or computational experiments. The macro has been tested on the data of a laboratory and numerical experiment on the synthesis of a styrene-butadiene copolymer. This copolymer is formed as a result of carrying out the process of copolymerization in continuous mode in a cascade of continuous stirred tank reactors. The results of the experiment are the characteristics of the formed product for each reactor of the cascade at the end of each hour of process modeling. Transferring data into a single file of a certain structure allows you to graphically present the results of the experiment and facilitates further analysis of the characteristics of the product being studied, depending on the formulation and process conditions.

Broad Learning Aided Model Predictive Control With Application to Continuous Stirred Tank Heater

This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of the system model. The predictive controller is realized by regressing the predictive model using online process data and the incremental broad learning algorithm. The proposed model predictive control (MPC) approach requires less online computational load compared to other neural network based MPC approaches. More importantly, the precision of the predictive model is enhanced with reduced computational load by operating an appropriate approximation of the predictive model. The approximation is proved to have no influence on the convergence of the predictive control algorithm. Compared with the partial form dynamic linearization aided model free control (PFDL-MFC), the control performance of the proposed predictive controller is illustrated through the continuous stirred tank heater (CSTH) benchmark.