Industrial Data Science for Batch Manufacturing Processes

Batch processes show several sources of variability, from raw materials' properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an industrial example how to use machine learning to reduce this apparent excess of data while maintaining the relevant information for process engineers. Two common use cases will be presented: 1) AutoML analysis to quickly find correlations in batch process data, and 2) trajectory analysis to monitor and identify anomalous batches leading to process control improvements

Lysophosphatidylcholine and Carotid Intima-Media Thickness in Young Smokers: A Role for Oxidized LDL-Induced Expression of PBMC Lipoprotein-Associated Phospholipase A2?

Although cigarette smoking has been associated with carotid intima-media thickness (CIMT) the mechanisms are yet not completely known. Lysophosphatidylcholine (lysoPC), a main product of lipoprotein-associated phospholipase A2 (Lp-PLA2) activity, appears to be a major determinant of the pro-atherogenic properties of oxidized LDL (oxLDL) and to induce proteoglycan synthesis, a main player in intimal thickening. In this study we assessed whether cigarette smoking-induced oxidative stress may influence plasma Lp-PLA2 and lysoPC and Lp-PLA2 expression in peripheral blood mononuclear cells (PBMC), as well as the relationship between lysoPC and CIMT.45 healthy smokers and 45 age and sex-matched subjects participated in this study. Smokers, compared to non-smokers, showed increased plasma concentrations of oxLDL, Lp-PLA2 and lysoPC together with up-regulation of Lp-PLA2 (mRNA and protein) expression in PBMC (P<0.001). Plasma Lp-PLA2 positively correlated with both lysoPC (r=0.639, P<0.001) and PBMC mRNA Lp-PLA2 (r=0.484, P<0.001) in all subjects. Moreover CIMT that was higher in smokers (P<0.001), positively correlated with lysoPC (r=0.55, P<0.001). Then in in vitro study we demonstrated that both oxLDL (at concentrations similar to those found in smoker's serum) and oxidized phospholipids contained in oxLDL, were able to up-regulate mRNA Lp-PLA2 in PBMC. This effect was likely due, at least in part, to the enrichment in oxidized phospholipids found in PBMC after exposure to oxLDL. Our results also showed that in human aortic smooth muscle cells lysoPC, at concentrations similar to those found in smokers, increased the expression of biglycan and versican, two main proteoglycans.In smokers a further effect of raised oxidative stress is the up-regulation of Lp-PLA2 expression in PBMC with subsequent increase of plasma Lp-PLA2 and lysoPC. Moreover the correlation between lysoPC and CIMT together with the finding that lysoPC up-regulates proteoglycan synthesis suggests that lysoPC may be a link between smoking and intimal thickening

Towards Sustainable Operation in the (Bio)chemical Industry: A Framework for Computer-aided Multi-objective Decision-making

In this research advanced numerical techniques and software are developed to support real-time decision making for the design and optimization of large-scale dynamic (bio)chemical processes. To achieve this aim different steps must be taken. First, improvements and extensions to current state-of-the-art methods for multi-objective optimization (MOO) are elaborated in order to obtain efficient solution strategies for optimizing dynamic (bio)chemical processes. More specically, adapted algorithmic schemes, parallelization strategies and Object-Oriented Programming have to be explored. Second, a software with a user-friendly interface has to be implemented in order to allow an eective application at the industrial level. Last, a validation of the provided techniques and software is required on industry relevant case studies to prove the possibly large contributions in, e.g., reducing costs, limiting waste, lowering energy consumption and improving safety.status: publishe

Tuning of NMPC controllers via multi-objective optimisation

Nonlinear Model Predictive Control (NMPC) is a powerful technique that can be used to control many industrial processes. Different and often conflicting control objectives, e.g., reference tracking, disturbance rejection and minimum control effort, are typically present. Most often these objectives are translated into a single weighted sum (WS) objective function. This approach is widespread because it is easy to use and understand. However, selecting an appropriate set of weights for the objective function is often non-trivial and is mainly done by trial and error. The current study proposes a systematic procedure for tuning Nonlinear MPC based on multi-objective optimisation methods. Advanced methods allow an efficient solution of the multi-objective problem providing a systematic overview of the controller behaviour. Moreover, through analytic relations it is possible to link a solution obtained with these novel methods to a set of weights for a weighted sum objective function. Applying this set of weights causes the WS to generate the same solution as obtained with the advanced method. Hence, an appropriate controller can be selected based on the alternatives generated by the advanced method, while the corresponding weights for a WS can be derived for implementing the controller in practice. The procedure is successfully tested on two benchmark applications: the Van de Vusse reactor and the Tennessee Eastman plant.publisher: Elsevier articletitle: Tuning of NMPC controllers via multi-objective optimisation journaltitle: Computers & Chemical Engineering articlelink: http://dx.doi.org/10.1016/j.compchemeng.2013.10.003 content_type: article copyright: Copyright © 2013 Elsevier Ltd. All rights reserved.status: publishe

Interactive NBI and (E)NNC methods for the progressive exploration of the criteria space in multi-objective optimization and optimal control

© 2015 Elsevier Ltd. A wide range of problems arising from real world applications present multiple and conflicting objectives to be simultaneously optimized. However, this multi-objective nature is too often neglected. Multi-objective optimization proved to be a powerful tool to correctly describe the trade-offs among conflicting objectives in a set of optimal solutions known as the Pareto set. This paper introduces an interactive method to solve multi-objective problems based on geometric considerations. The method returns a wider Pareto set, at a negligible computational cost, when compared to existing methods. The interactivity also allows the decision-maker to explore only relevant parts of the Pareto set. The extreme solutions yield insightful considerations on the generation of the scalarization parameters for the Normal Boundary Intersection and the Enhanced Normalized Normal Constraints methods. The proposed method is applied to: (i) three scalar multi-objective problems and (ii) the multi-objective optimal control of a tubular and a fed-batch reactor.publisher: Elsevier articletitle: Interactive NBI and (E)NNC methods for the progressive exploration of the criteria space in multi-objective optimization and optimal control journaltitle: Computers & Chemical Engineering articlelink: http://dx.doi.org/10.1016/j.compchemeng.2015.07.004 content_type: article copyright: Copyright © 2015 Elsevier Ltd. All rights reserved.status: publishe

An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty

© 2015 Elsevier Ltd. All rights reserved. The manufacturing industry is faced with the challenge to constantly improve its processes, e.g., due to lower profit margins, more strict environmental policies and increased societal awareness. These three aspects are considered as the pillars of sustainable development and typically give rise to multiple and conflicting objectives. Hence, any decision made will require trade-offs to be evaluated and compromises to be made. To support decision making an interactive multi-objective framework is presented to optimize dynamic processes based on mathematical models. The framework includes a numerically efficient strategy to account for parametric uncertainty in the models and it allows to directly minimize the operational risks arising from this uncertainty. Hence, for the first time expert knowledge on the trade-offs between traditional objective functions and operational risks is readily and interactively available for the practitioners in the field of dynamic systems. The introduced interactive framework for multi-objective dynamic optimization under uncertainty is successfully tested for a three and five-objective fed-batch reactor case study with uncertain feed temperature and heat transfer parameters.Mattia Vallerio has a Ph.D. Grant of the Agency for Innovation through Science and Technology in Flanders (IWT). Jan Van Impe held the chair Safety Engineering sponsored by the Belgian Chemistry and Life Sciences Federation essenscia. The research was supported by KUL, PFV/10/002 (OPTEC), the Flemish Government via FWO-projects: FWO KAN2013 1.5.189.13, FWO-G.0930.13, the Belgian Federal Science Policy Office: IAP VII/19 (DYSCO).status: publishe

Metodi numerici avanzati per il controllo e l’ottimizzazione multi-obiettivo nell’industria (bio)chimica

Advanced model based techniques for computer aided decision making proved their value in the last decades, leading to increased safety and production, and lowered energy consumption. These different goals are often in conflict with each other, and most likely they are incommensurable. Hence, our research explores the advantages of exploiting a multi-objective approach to generate a set of optimal alternatives (Pareto set). However, the currently used multi-objective optimization methods often inefficiently generate Pareto optimal alternatives (e.g., the Weighted Sum), or are computationally expensive. Moreover, the complex and dynamic nature of (bio)chemical processes typically gives rise to large-scale nonlinear dynamic optimization problems, while safety requirements rise the question for robust optimization solutions, which statistically guarantee the satisfaction of constraints despite possible model uncertainty. Case studies are presented that illustrate the huge potential of such an approach for optimal control problems in the (bio)chemical industry

Multi-objective Optimal Control of Chemical Processes using ACADO Toolkit

Many practical chemical engineering problems involve the determination of optimal trajectories given multiple and conflicting objectives. These conflicting objectives typically give rise to a set of Pareto optimal solutions. To enhance real-time decision making efficient approaches are required for determining the Pareto set in a fast and accurate way. Hereto, the current paper illustrates the use of the freely available toolkit ACADO Multi-Objective (www.acadotoolkit.org) on several chemical examples. The rationale behind ACADO Multi-Objective is the integration of direct optimal control methods with scalarisation-based multi-objective methods enabling the exploitation of fast deterministic gradient-based optimisation routines. © 2011 Elsevier Ltd.status: publishe

A study of integrated experiment design for NMPC applied to the Droop model

Nonlinear model predictive control (NMPC) has become an important tool for optimization based control of many (bio)chemical systems. A requirement for a well performing NMPC implementation is obtaining and maintaining an appropriate mathematical process model. To cope with model degradation in view of plant changes and/or system evolution, developments have been made for linear systems to incorporate the information content of future measurements in the closed loop objective. However, formulations for integrated experiment design in nonlinear systems (iED-NMPC) remain scarce. Two different formulations are studied in this paper and applied to a bioprocess, namely, algae growth as described by the Droop model. First, a formulation for the integration of experiment design in linear dynamic systems is extended to nonlinear dynamic systems resulting in an NMPC formulation with integrated experiment design. In a second approach, the notion of economic optimal experiment design is incorporated within the NMPC formulation. Here, an economic loss function related to inaccurate parameter estimates is minimized instead of a measure of the parameter variances, resulting in improved control performance. The advantage of the proposed techniques over a naive experiment design integration approach is illustrated with Monte Carlo simulations.publisher: Elsevier articletitle: A study of integrated experiment design for NMPC applied to the Droop model journaltitle: Chemical Engineering Science articlelink: http://dx.doi.org/10.1016/j.ces.2016.10.046 content_type: article copyright: © 2016 Published by Elsevier Ltd.status: publishe

Towards nonlinear model predictive control with integrated experiment design

© 2016 American Automatic Control Council (AACC). Nonlinear model predictive control (NMPC) has become an important tool in the control and optimization of nonlinear systems in a variety of engineering applications. A requirement for a well-performing NMPC implementation is obtaining and maintaining an appropriate mathematical model of the considered system. For linear dynamic systems, developments have been made to incorporate information content objectives in closed loop, i.e., to solve the dual control problem. However, formulations for nonlinear dynamic systems remain scarce. In this paper we extend the formulation for the integration of experiment design of linear dynamic systems to nonlinear dynamic systems resulting in a NMPC formulation with integrated experiment design (iED-NMPC). This results for nonlinear systems in the presence of a nonlinear matrix inequality. We propose to reformulate this nonlinear matrix inequality using Sylvester's criterion. The suggested approach allows us to replace the nonlinear matrix inequality by additional nonlinear constraints. The resulting formulation can subsequently be implemented in existing NMPC software packages.status: publishe