Achieving more efficient operation often means adjusting several variables that might conflict with each other, e.g., better quality sometimes mandates a lower production rate and higher energy consumption. So, determining the best operating conditions requires knowledge of the quantitative effects of several variables on parameters of interest. These relations tend to be complicated. Physical models usually don't describe industrial production processes accurately enough. On the other hand, empirical and semi-empirical modeling approaches don't demand any significant assumptions or simplifications.
Conventional empirical modeling relies on linear statistical techniques that have severe limitations. In contrast, new nonlinear modeling techniques can derive knowledge about complicated nonlinear effects of variables from production data, taking nonlinearities into account. Such nonlinear models of a tall oil distillation column implemented at Forchem Oy in Finland have helped improve the operation of the process.
Tall or pine oil is a byproduct of pulping of pine wood via the Kraft process. Tall oil contains mostly acidic but also some neutral components. Forchem uses a series of distillation columns to separate it into rosin, fatty acids, heavy oils and pitch. Rosin consists mainly of rosin acids, particularly abietic and pimaric acids, which also are referred to as resin acids. For rosin production, water and turpentine are first removed from the raw tall oil received from a pulp mill. The tall oil then passes to a distillation column that produces raw fatty acids as the overhead stream and a bottoms containing mostly rosin. A portion of this heavy fraction goes to the pitch column. Rosin is taken out somewhat above the bottom. This distillation is energy intensive and significantly affects profitability of production.
The acid value of tall oil is typically around 140 while the rosin acid content is about 30% for Forchem's raw material. Rosin distilled from this oil typically contains 90% rosin acids and has a softening point of around 60°C–65°C.
WHY NONLINEAR MODELING?
Better quantitative knowledge of the effects of process variables and feed characteristics can enable efficiency improvements. However, these relationships tend to be complicated for distillation processes, like many industrial operations. Physical modeling only provides modest accuracies in predicting most of the interesting consequences of distillation like product purity and energy consumption — partly because it requires plenty of assumptions and simplifications, and partly because sufficient knowledge of the thermodynamics of each component in tall oil is lacking. In constrast, empirical and semi-empirical modeling suffers neither of these limitations. It just needs an adequate amount of production data with a fair variation in the variables of interest.
Simpler nonlinear modeling approaches include polynomial regression and linear regression with nonlinear terms. Nonlinear regression is useful in some situations. However, these older techniques require specifying the form of the nonlinearities. Newer techniques, including series of basis functions, splines, kernel regression, and feed-forward neural networks, are based on free-form nonlinearities.
To try to improve the tall oil distillation process, we chose an empirical approach based on feed-forward neural networks. Such networks are a set of efficient tools for nonlinear modeling, particularly because of their universal approximation capability . They can be applied to individual unit operations or entire trains, whether batch or continuous, and regardless of the type of chemical or other process .