A comparative study between the use of artificial neural networks and multiple linear regression for caustic concentration prediction in a stage of alumina production

• Autor(a) principal: Rozza, Giovanni Leopoldo
• Data de Publicação: 2015
• Outros Autores: da Silva, Ruy Gomes, Müller, Sonia Isoldi Marty Gama
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Revista Produção Online
• Texto Completo: https://www.producaoonline.org.br/rpo/article/view/1941
• topic: Artificial Neural Networks. Multiple Linear Regression. Evaporation. Bayer Process. Alumina.; Redes Neurais Artificiais. Processo Bayer. Regressão Linear Múltipla. Alumina. Evaporação.

With world becoming each day a global village, enterprises continuously seek to optimize their internal processes to hold or improve their competitiveness and make better use of natural resources. In this context, decision support tools are an underlying requirement. Such tools are helpful on predicting operational issues, avoiding cost risings, loss of productivity, work-related accident leaves or environmental disasters. This paper has its focus on the prediction of spent liquor caustic concentration of Bayer process for alumina production. Caustic concentration measuring is essential to keep it at expected levels, otherwise quality issues might arise. The organization requests caustic concentration by chemical analysis laboratory once a day, such information is not enough to issue preventive actions to handle process inefficiencies that will be known only after new measurement on the next day. Thereby, this paper proposes using Multiple Linear Regression and Artificial Neural Networks techniques a mathematical model to predict the spent liquor´s caustic concentration. Hence preventive actions will occur in real time. Such models were built using software tool for numerical computation (MATLAB) and a statistical analysis software package (SPSS). The models output (predicted caustic concentration) were compared with the real lab data. We found evidence suggesting superior results with use of Artificial Neural Networks over Multiple Linear Regression model. The results demonstrate that replacing laboratorial analysis by the forecasting model to support technical staff on decision making could be feasible.