Making Industrial Processes More Cost Efficient
Economic conditions drive industries towards more efficient operation of their production processes. More efficient operation often means improvements in several variables which might conflict with each other, like quality sometimes comes at the cost of production rate and higher energy consumption. To determine the best operating conditions, it is necessary to have the knowledge of quantitative effects of several variables on consequences of interest. These relations tend to be complicated, and physical models do not usually describe industrial production processes accurately enough. Empirical and semi-empirical modeling approaches, on the other hand, do not necessitate any significant assumptions or simplifications.
Conventional techniques of empirical modeling are linear statistical techniques, which have severe limitations. New techniques of nonlinear modeling are capable of deriving knowledge about complicated nonlinear effects of variables from production data, taking nonlinearities into account. This article describes how nonlinear models of a tall oil distillation column implemented in Forchem Oy in Finland helps improve the operation of the process.