The issue of outer model weight updating is important in extending partial least squares (PLS) regression to modelling data that shows significant non-linearity. This paper presents a novel co-evolutionary component approach to the weight updating problem. Specification of the non-linear PLS model is achieved using an evolutionary computational (EC) method that can co-evolve all non-linear inner models and all input projection weights simultaneously. In this method, modular symbolic non-linear equations are used to represent the inner models and binary sequences are used to represent the projection weights. The approach is flexible, and other representations could be employed within the same co-evolutionary framework. The potential of these methods is illustrated using a simulated pH neutralisation process data set exhibiting significant non-linearity. It is demonstrated that the co-evolutionary component architecture can produce results which are competitive with non-linear neural network-based PLS algorithms that use iterative projection weight updating. In addition, a data sampling method for mitigating overfitting to the training data is described.

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This paper reports the influence of reaction temperature on the occurrence and characteristics of pH oscillations that are observed during the palladium-catalysed phenylacetylene oxidative carbonylation reaction in a catalytic system (PdI2, KI, air, NaOAc) in methanol. Isothermal experiments were performed over the temperature range 10-50 degrees C. The experiments demonstrate that oscillations occur in the range 10-40 degrees C and that a decrease in reaction temperature results in an increase in the period and amplitude of the pH oscillations. Furthermore, it is observed that during oscillations at any specific temperature, the time taken for pH to increase from a minimum to a maximum value varies with respect to reaction time. However, the time required for the pH to fall from maximum to new minimum is approximately constant with respect to the reaction time and is a function of the reaction temperature.