Computation Intelligence has revolutionized the way that engineers in the process industry solve highly complex problems. In the past, modeling and optimizing of chemical processes and materials characteristics were done by developing a fundamental model or a statistical model. Fundamental models often required years of research. Furthermore, the calculations of these models were often too time-consuming to be used for online optimization and control. Statistical models, again, required the availability of good data that could be linearised. Many industrial data sets turned out to be too noisy and high-dimensional to be solved with statistical techniques.
The introduction of Neural Networks (NN) as a new tool to quickly model highly nonlinear processes marked a clear turning point in the chemical industry. Since then, computational intelligence methods, like NN, Support Vector Machines (SVM) and Genetic Programming (GP), have been applied to a wide variety of problems in the process industry. These methods have not only become essential in the set of tools available to solve industrial problems, but also generated millions of dollars in profit due to improved process operability.
The list successful applications at the Dow Chemical Company include:
- A NN-application to predict NOx-emissions.
- Outlier detection using SVM.
- A GP-model to predict the biomass concentration in a batch reactor.
- Using GP to help developing new rheological insights.
- Particle Swarm Optimization (PSO) for optimizing properties of polymers.

Elsa Jordaan received her PhD in 2002 from the Eindhoven University of Technology, Netherlands, with a thesis on the development of robust inferential sensors and industrial applications of support vector machines. Since then she has been working as a research specialist in process optimization at the Dow Chemical Company's manufacturing site in the Netherlands. She is involved in many projects where nonlinear modeling or high-dimensional data analysis is required. Other application areas include industrial statistics, risk analysis, optimization of energy and feedstock needs, and freight and logistics cost modelling. Her current area of research is in the safeguarding of data-driven models in an online environment.

Elsa is author of numerous conference publications, a book chapter and invited talks on the subject of applications of computational intelligence in the chemical industry. Dr. Jordaan is an active member of the IEEE CIS AdHoc Committee on Technology Transfer and advisory board member of the Birmingham University (UK) M.Sc Program in Natural Computation.