The process models are developed from data on feed characteristics, process variables, as well as properties and flow rates of outgoing streams, to determine the best values of process variables to maintain operation within desired limits while also satisfying economic criteria (such as maximum yield, purity, production rate or energy efficiency, and minimum raw material consumption or defects). Many books, including Ref. 3, describe various techniques for this kind of optimization problem.
DEVELOPING THE MODELS
We identified a number of variables to use for developing the nonlinear models, and had production data from January 2009 onwards. Production data tend to be dilute in information and often contain misleading observations. So, we carefully selected data, excluding some observations from the model development set. Doing so enabled us to make more progress in a shorter time period — the model development project started in February 2011 and finished in June. Figure 1 shows the kinds of variables included in the models. Testing indicated the models were good quality.
Figure 2 compares the measured values of energy consumption of the distillation with values predicted by the nonlinear model. These nonlinear models then were implemented in a LUMET system, a set of software components meant to make use of the models easy for people not familiar with nonlinear modeling.
The models allow us to see the individual and combined effects of the input variables on the outputs. Figure 3 shows the interesting cross-effect of feed flow rate and rosin acid content in raw fatty acids. At lower rosin contents, higher feed flow rates are preferable for a given production rate. However, for higher rosin contents, lower feed flow rates lead to smaller losses of rosin acids through the fatty acid stream. For the typical case of a medium amount of rosin in the feed, there is an optimum feed flow rate that minimizes loss of rosin acids in the raw fatty acid stream. Ideally, of course, we strive to maximize rosin and raw fatty acid flow rates while trying to get as much of the rosin acids as possible in the rosin and minimizing the rosin acids in the raw fatty acid stream. Some input variables have desirable effects on some outputs but undesirable effects on others. These and other such conflicting objectives make it a challenging task to determine the best ways of operating the column.
Mathematical models can contain a lot of valuable quantitative knowledge in a concise and a precise manner. However, concrete benefits only result if the models are used efficiently. LUMET systems, which have been developed over 15 years, offer several features that make it easier to utilize the model for several kinds of calculations.
One shift supervisor, the fourth author of this article, expects the models to help both experienced as well as inexperienced operators operate the column better — particularly when dealing with uncommon situations. The production director, the second author, believes operators' use of the models should lead to higher yields and lower energy consumption while ensuring quality in terms of softening point and rosin acid content of the rosin.
The nonlinear models have helped improve the operation of the distillation column right from the first day. Seven and a half hours after the LUMET system went into use, rosin yield was increased by more than 8%, energy consumption was lowered by about 6%, and the rosin acid loss through the raw fatty acid stream was reduced by around 0.3%. This very good improvement can't be expected every time. However, over the following days, a rosin yield increase of about 3% usually was possible with little effort and without much change in energy consumption. Increased production from a boost in yield of only 1.5% conservatively is worth more than €500,000 ($700,000) per year. The use of the models essentially has raised rosin production capacity as well as operating efficiency.
CONSIDER NONLINEAR MODELS
Plants usually collect a lot of production data that go unused or under-utilized. Nonlinear modeling provides a way to extract valuable information from those data — and thus for plants to get more out of their equipment and raw materials while also improving control over product properties. If used effectively, it can add to a company's competitiveness.
ABHAY BULSARI is technical manager for Nonlinear Solutions Oy, Turku, Finland. TIMO SAARENKO is production director, JAANA VALTANEN is a quality assurance engineer, and LAURA KASKINEN is a shift supervisor for Forchem Oy, Rauma, Finland. E-mail them at email@example.com, firstname.lastname@example.org, email@example.com and firstname.lastname@example.org.
1. Hornik, K., Stinchcombe, M. and White, H., "Multilayer Feedforward Networks Are Universal Approximators," Neural Networks, Vol. 2, p. 359 (1989).
2. "Neural Networks for Chemical Engineers," A. Bulsari, ed., Elsevier, Amsterdam (1995).
3. Gill, P. E., Murray, W. and Wright, M. H., "Practical Optimisation," p. 136, Academic Press, London (1981).