Stability criterion for the intensification of batch processes with model predictive control

Thermal runaways in batch processes can lead to significant issues for safety and performance during normal operation in industry. This is usually circumvented by running such processes at lower temperatures than necessary, hence losing the opportunity to intensify production and therefore reduce reaction time. The detection of the thermal stability of batch systems can potentially be embedded in an advanced control scheme, therefore improving the performance by being able to intensify the process, achieving higher yields while keeping a stable operation. The derivation of stability criterion K for high-order reactions is presented in this work, resulting in better control when embedded in Model Predictive Control (MPC) schemes than standard nonlinear MPC schemes, based on the work in Kahm and Vassiliadis (2018). The non-trivial extension of stability criterion K for multi-component reactions with application to MPC systems is discussed in detail. The logic and verification of the form of the resultant Damkohler number in particular is discussed and demonstrated with case studies. A comparison of various MPC schemes is presented, showcasing that the implementation using criterion K results in intensified processes kept stable at all times, whilst reducing computational cost with regards to standard nonlinear MPC schemes. Furthermore, reaction times are reduced by at least two-fold with respect to processes run at constant temperatures

Chlamydomonas reinhardtii Metabolic Pathway Analysis for Biohydrogen Production under Non-Steady-State Operation

This paper presents a novel structured dynamic model to simulate the metabolic reaction network of green algae hydrogen production from aerobic condition to anaerobic condition, which has not been addressed in the open literature to this date. An efficient parameter estimation methodology is proposed to avoid the difficulty of measuring essential kinetic parameters from experiments. The accuracy of the model is verified by comparison to published experimental results. The current model finds that the starch generation pathway mainly competes with hydrogen production pathway, as its activity is enhanced by the cyclic electron flow pathway. From the dynamic sensitivity analysis, it is concluded that the most effective solution to enhance hydrogen production is to seek the optimal sulphur concentration in the culture, rather than to modify the activity of specific enzymes. The current work also denies the previous hypothesis that the diffusion of small proteins in the metabolic network inhibits hydrogen production.This is the accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acs.iecr.5b0203

Thermal stability criterion integrated in model predictive control for batch reactors

Thermal runaways can have a significant impact on the performance and normal operation of reaction processes, causing safety issues and financial loss, which hinder the intensification of such processes. More specifically, a control system that does not possess proper detection mechanisms of the boundary of stability will by necessity be overly conservative. This leads to poorer performance and the inability to intensify the process, i.e. to reduce process times for example and also to achieve higher yields. For the intensification of batch processes a stability criterion, based on the divergence criterion, is presented. The derivation of the stability criterion and a comparison to the original divergence criterion is shown for several batch reactions. It is shown that the stability criterion classifies the system behaviour more reliably for the case studies considered. This stability criterion is embedded in Model Predictive Control, which is a novel control scheme. This scheme allows the controlled increase of the reaction temperature while keeping the processes in a stable region, hence reducing the risk of thermal runaways. This control system enables batch processes to achieve a target conversion in a reduced completion time of reaction and an intensification of batch processes.Studentship from Department of Chemical Engineering/EPSR

Optimal Laypunov exponent parameters for stability analysis of batch reactors with Model Predictive Control

Thermal runaways in exothermic batch reactions are a major economic, health and safety risk in industry. In literature most stability criteria for such behaviour are not reliable for nonlinear non-steady state systems. In this work, Lyapunov exponents are shown to predict the instability of highly nonlinear batch processes reliably and are hence incorporated in standard MPC schemes, leading to the intensification of such processes. The computational time is of major importance for systems controlled by MPC. The optimal tuning of the initial perturbation and the time frame reduces the computational time when embedded in MPC schemes for the control of complex batch reactions. The optimal tuning of the initial perturbation and time horizon, defining Lyapunov exponents, has not been carried out in literature so far and is here derived through sensitivity analyses. The computational time required for this control scheme is analysed for the intensification of complex reaction schemes

Thermal stability criterion of complex reactions for batch processes

Thermal stability of batch processes is a major factor for the safe and efficient production of polymers and pharmaceutical chemicals. The prediction of the thermal stability for such processes was shown in Kahm and Vassiliadis (2018d) to be unreliable with most stability criteria found in literature also presenting a novel criterion, K, which was shown to give reliable stability predictions for single reactions of higher order. This work provides a detailed derivation for the generalization of thermal stability criterion K applied to reaction networks of arbitrary complexity, consisting of parallel and competing reactions of both exothermic and endothermic nature. The generalized thermal stability criterion K is then applied to Model Predictive Control (MPC) frameworks to intensify batch processes in a safe manner, reducing the time required to reach the target conversion. Several illustrative computational case studies are presented, highlighting the proposed methodology and verifying its validity

Automated structure detection for distributed process optimization

The design and control of large-scale engineering systems, consisting of a number of interacting subsystems, is a heavily researched topic with relevance both for industry and academia. This paper presents two methodologies for optimal model-based decomposition, where an optimization problem is decomposed into several smaller sub-problems and subsequently solved by augmented Lagrangian decomposition methods. Large-scale and highly nonlinear problems commonly arise in process optimization, and could greatly benefit from these approaches, as they reduce the storage requirements and computational costs for global optimization. The strategy presented translates the problem into a constraint graph. The first approach uses a heuristic community detection algorithm to identify highly connected clusters in the optimization problem graph representation. The second approach uses a multilevel graph bisection algorithm to find the optimal partition, given a desired number of sub-problems. The partitioned graphs are translated back into decomposed sets of sub-problems with a minimal number of coupling constraints. Results show both of these methods can be used as efficient frameworks to decompose optimization problems in linear time, in comparison to traditional methods which require polynomial time.Author E. A. del Rio-Chanona would like to acknowledge CONACyT scholarship No. 522530 for funding this project. Author F. Fiorelli gratefully acknowledges the support from his family. The authors would also 27 like to thank Dr Bart Hallmark, University of Cambridge, for suggesting to employ as a demonstration the chemical system in Example 7.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.compchemeng.2016.03.01

Metabolite biomarker discovery for metabolic diseases by flux analysis

Metabolites can serve as biomarkers and their identification has significant importance in the study of biochemical reaction and signalling networks. Incorporating metabolic and gene expression data to reveal biochemical networks is a considerable challenge, which attracts a lot of attention in recent research. In this paper, we propose a promising approach to identify metabolic biomarkers through integrating available biomedical data and disease-specific gene expression data. A Linear Programming (LP) based method is then utilized to determine flux variability intervals, therefore enabling the analysis of significant metabolic reactions. A statistical approach is also presented to uncover these metabolites. The identified metabolites are then verified by comparing with the results in the existing literature. The proposed approach here can also be applied to the discovery of potential novel biomarkers. © 2012 IEEE.published_or_final_versio

Simulation and optimization of dynamic flux balance analysis models using an interior point method reformulation

This work presents a novel, differentiable, way of solving dynamic Flux Balance Analysis (dFBA) problems by embedding flux balance analysis of metabolic network models within lumped bulk kinetics for biochemical processes. The proposed methodology utilizes transformation of the bounds of the embedded linear programming problem of flux balance analysis via a logarithmic barrier (interior point) approach. By exploiting the first-order optimality conditions of the interior-point problem, and with further transformations, the approach results in a system of implicit ordinary differential equations. Results from four case studies, show that the CPU and wall-times obtained using the proposed method are competitive with existing state-of-the-art approaches for solving dFBA simulations, for problem sizes up to genome-scale. The differentiability of the proposed approach allows, using existing commercial packages, its application to the optimal control of dFBA problems at a genome-scale size, thus outperforming existing formulations as shown by two dynamic optimization case studies.• R. Conejeros would like to thank CONICYT’s research grant FONDECYT 1151295 for funding this research. • F. Scott gratefully acknowledges financial support from CONICYT (Proyectos REDES ETAPA INICIAL, Convocatoria 2017, REDI170254)