Optimal control of enzymatic hydrolysis of lignocellulosic biomass

Cellulose hydrolysis is a key step in lignocellulosic ethanol production. At present, commercial production of lignocellulosic ethanol is limited due to the long hydrolysis times and requirement of large quantity of expensive enzymes. Therefore, reduction of the enzyme consumption as well as hydrolysis time is crucial and model based optimisation methods can be used for the same. A semi-mechanistic model with cellobiose, glucose, and xylose inhibition with Arrhenius based relationship between temperature and kinetic parameters and thermal deactivation of enzymes was used for the present study. Optimal control problem with temperature as control variable was formulated after considering two different objective functions. For the objective of glucose concentration maximisation at final batch time, the benefit of implementing optimal control increased with reducing batch times. For the batch time of 24 hours, the final glucose concentration increased by 3.2%. For the objective of batch time minimisation, the reduction of batch time was 5.8% and it was observed for a target glucose concentration of 45 g/kg of cellulose. The use of optimal control can reduce the enzyme requirement up to 77.8% of endoglucanase and exoglucanase for glucose maximisation and 22.2% for batch time minimisation. The above results show the usefulness of optimal temperature control in increasing the glucose concentration, and reducing the batch time without increasing the enzyme used

Sensitivity analysis and stochastic modelling of lignocellulosic feedstock pretreatment and hydrolysis

Pretreatment and hydrolysis of lignocellulosic biomass are affected by several uncertainties, which must be systematically considered for a robust process design. In this work, stochastic simulations for expected uncertainties in feedstock composition, kinetic parameter values, and operational parameter values for these two steps were performed. The results indicated that these uncertainties significantly impacted the concentration profiles, which could also affect the optimal batch time. Global sensitivity analysis was then used to identify the critical uncertain parameters. In the feedstock components, cellulose and xylan fractions for acid pretreatment and cellulose fraction for enzymatic hydrolysis were important. Temperature was the most sensitive operating parameter for both acid pretreatment and hydrolysis. The activation energies for different reactions were ranked in terms of their impact on process output. The selected parameters were used to develop stochastic process models using Ito process and mean reverting process for feed composition and kinetic parameter uncertainty. (C) 2017 Elsevier Ltd. All rights reserved

Studies on crystallization process for pharmaceutical compounds using ANN modeling and model based control

Solvent selection and Controlling of operating parameters play a crucial role in batch cooling crystallization process. Choosing a best solvent for crystallization process involves more experimentation and time. To overcome this problem, an Artificial Neural Network (ANN) model technique is used to predict the carbamazepine form Ⅲ solubility by considering the thermodynamic properties of different solvents i.e. critical temperature, critical pressure, temperature, molecular weight, and acentric factor. The ANN model was trained and evaluated for solubility at various input data sets using experimental solubility data available in the literature. The ANN model with 20 hidden neurons has given the R2 value of 0.9943 which shows that the developed ANN model can be used for the selection of best solvent for batch crystallization process. Further, to determine the optimal cooling profile of batch cooling crystallization process, a multi-objective optimization problem is formulated by considering objectives as minimizing the coefficient of variation (CV) and maximizing the Number mean size (NMS) of crystals subjected to population balance equations using “method of moments” technique. Two types of temperature strategies i.e., piece-wise constant and piece-wise linear are developed and solved using NSGA-Ⅱ dynamic optimization procedure. The optimal NMS value attained through piece-wise linear strategy was 197.1 µm. This value has been increased by 28.3 µm from the nominal case (without optimization) and the coefficient of variation has decreased from 0.951 to 0.76. Further, optimal NMS value attained through piece-wise constant strategy was 205 µm. The value has been increased by 36.2 µm and the coefficient of variation has decreased from 0.951 to 0.73. This proves that the crystal attributes can be improved by optimal cooling temperature profile obtained by multi-objective optimization framework. For implementing the optimal cooling profile an advanced model-based control, i.e., Generic Model Control (GMC) was developed. It was observed that the GMC controller has the good tracking profile with no offset with/without disturbances and small value of root mean square error (RMSE) of 0.0016 using piece-wise constant as set point temperature. Using piece-wise linear as set point temperature, the RMSE value was 0.0018. In particular, it is advantageous to operate the batch cooling crystallization process with piece-wise linear strategy for set point trajectory tracking problems

Digital Twins for Bioprocess Control Strategy Development and Realisation

New innovative Digital Twins can represent complex bioprocesses, including the biological, physico-chemical, and chemical reaction kinetics, as well as the mechanical and physical characteristics of the reactors and the involved peripherals. Digital Twins are an ideal tool for the rapid and cost-effective development, realisation and optimisation of control and automation strategies. They may be utilised for the development and implementation of conventional controllers (e.g. temperature, dissolved oxygen, etc.), as well as for advanced control strategies (e.g. control of substrate or metabolite concentrations, multivariable controls), and the development of complete bioprocess control. This chapter describes the requirements Digital Twins must fulfil to be used for bioprocess control strategy development, and implementation and gives an overview of research projects where Digital Twins or "early-stage" Digital Twins were used in this context. Furthermore, applications of Digital Twins for the academic education of future control and bioprocess engineers as well as for the training of future bioreactor operators will be described. Finally, a case study is presented, in which an "early-stage" Digital Twin was applied for the development of control strategies of the fed-batch cultivation of Saccharomyces cerevisiae. Development, realisation and optimisation of control strategies utilising Digital Twins

Purinergic signaling in inflammatory cells: P2 receptor expression, functional effects, and modulation of inflammatory responses

Extracellular ATP and related nucleotides promote a wide range of pathophysiological responses via activation of cell surface purinergic P2 receptors. Almost every cell type expresses P2 receptors and/or exhibit regulated release of ATP. In this review, we focus on the purinergic receptor distribution in inflammatory cells and their implication in diverse immune responses by providing an overview of the current knowledge in the literature related to purinergic signaling in neutrophils, macrophages, dendritic cells, lymphocytes, eosinophils, and mast cells. The pathophysiological role of purinergic signaling in these cells include among others calcium mobilization, actin polymerization, chemotaxis, release of mediators, cell maturation, cytotoxicity, and cell death. We finally discuss the therapeutic potential of P2 receptor subtype selective drugs in inflammatory conditions