Feature extraction for batch process monitoring and fault detection via simultaneous data scaling and training of tensor based models

This contribution presents a novel framework for process monitoring and fault detection of batch processes. The batch platform is key for the full scale production of specialty chemicals, active pharmaceutical ingredients, biochemical products and other high added value products, where flexibility is required due to the relatively limited life span of the products and/or the diversity on big product portfolios of relatively small scale. Traditional approaches for fault identification in batch processes require unfolding the third order structure of the data (variables × time × batches). In this contribution a tensor based approach is considered because it applies a more comprehensive multilinear analysis of the data. The novel approach allows to impose independence relations through knowledge based structural sparsity constraints on the tensor decomposition. Additionally, a novel methodology is presented for simultaneous data scaling and model training. This allows finding the optimal data scale to exploit the variability present in all directions (i.e., from every variable). The combination of the two proposed methods results in an improved framework to extract interpretable features from the data. The application of the proposed novel framework to the dynamic simulation of the fed-batch penicillin production (Pensim) allows to evaluate its advantages over traditional methods for multivariate statistical process monitoring (MSPM). Some identified advantages of the novel framework are a better distribution on the approximation of the data with lower noise propagation and bias, as well as a higher interpretability of the extracted features and the fault detection. This results in an enhanced capability for monitoring batch processes and fault detection.