Hybrid modeling of a biorefinery separation process to monitor short-term and long-term membrane fouling

Membrane filtration is commonly used in biorefineries to separate cells from fermentation broths containing the desired products. However, membrane fouling can cause short-term process disruption and long-term membrane degradation. The evolution of membrane resistance over time can be monitored to track fouling, but this calls for adequate sensors in the plant. This requirement might not be fulfilled even in modern biorefineries, especially when multiple, tightly interconnected membrane modules are used. Therefore, characterization of fouling in industrial facilities remains a challenge. In this study, we propose a hybrid modeling strategy to characterize both reversible and irreversible fouling in multi-module biorefinery membrane separation systems. We couple a linear data-driven model, to provide high-frequency estimates of trans-membrane pressures from the available measurements, with a simple nonlinear knowledge-driven model, to compute the resistances of the individual membrane modules. We test the proposed strategy using real data from the world's first industrial biorefinery manufacturing 1,4-bio-butanediol via fermentation of renewable raw materials. We show how monitoring of individual resistances, even when done by simple visual inspection, offers valuable insight on the reversible and irreversible fouling state of the membranes. We also discuss the advantage of the proposed approach, over monitoring trans-membrane pressures and permeate fluxes, from the standpoints of data variability, effect of process changes, interaction between module in multi-module systems, and fouling dynamics

Digital design of new products: accounting for output correlation via a novel algebraic formulation of the latent-variable model inversion problem

Product design problems often require finding the raw materials and/or operating conditions (inputs) that are needed to achieve some pre-assigned quality specifications on the product (outputs). The problem can be tackled by first building a latent-variable model (e.g., partial least-squares regression) on historical manufacturing data of products similar to the new one, and then using model inversion to find the input conditions required to obtain the target product. However, in most practical cases, the variables characterizing product quality are correlated, and this may raise singularity issues upon algebraic model inversion. The most popular approach to cope with this relies on removing a priori some of the correlated quality variables from the model output matrix, and on building the latent-variable model in such a way that the inputs be related to the remaining outputs only. However, in this case the inputs obtained upon model inversion may not be able to ensure that the quality variables that were not included in the output matrix will be close enough to their targets. We propose a novel algebraic formulation of the latent-variable model inversion problem, named regularized direct inversion, which can cope with output correlation by design. The proposed formulation enables one to retain in the model output matrix all quality variables, and addresses output correlation by removing a posteriori only the non-systematic information that would cause singularity issues. Therefore, no structural information about the relation between inputs and out-puts is left out of the model by design, which in turn improves the performance of model inversion. The supe-riority of regularized direct inversion over the standard approach, and its ability to cope with model uncertainty, are proved using two simulated batch processes: a generic fermentation process, and a process for the manufacturing of penicillin