Automatic procedure for modelling, calibration, and optimization of a three-component chromatographic separation

The current landscape of biopharmaceutical production necessitates an ever-growing set of tools to meet the demands for shorter development times and lower production costs. One path towards meeting these demands is the implementation of digital tools in the development stages. Mathematical modelling of process chromatography, one of the key unit operations in the biopharmaceutical downstream process, is one such tool. However, obtaining parameter values for such models is a time-consuming task that grows in complexity with the number of compounds in the mixture being purified. In this study, we tackle this issue by developing an automated model calibration procedure for purification of a multi-component mixture by linear gradient ion exchange chromatography. The procedure was implemented using the Orbit software (Lund University, Department of Chemical Engineering), which both generates a mathematical model structure and performs the experiments necessary to obtain data for model calibration. The procedure was extended to suggest operating points for the purification of one of the components in the mixture by means of multi-objective optimization using three different objectives. The procedure was tested on a three-component protein mixture and was able to generate a calibrated model capable of reproducing the experimental chromatograms to a satisfactory degree, using a total of six assays. An additional seventh experiment was performed to validate the model response under one of the suggested optimum conditions, respecting a 95 % purity requirement. All of the above was automated and set in motion by the push of a button. With these results, we have taken a step towards fully automating model calibration and thus accelerating digitalization in the development stages of new biopharmaceuticals.

Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization

Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure are generated by an in-house-developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and the identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization condition. To decrease the execution time, clustering of the search space based on MMDs is applied. The performance of the proposed approach is tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives.

Optimizing the Heat Loss from an Insulation Material and Boundary Layer Thickness of Airflow through a Hot Plate Using Nonlinear Least-Squares Error and Linear Programming Algorithms

Heat loss is a major challenge in heat transfer problems. Several researchers have minimized heat loss for different heat transfer cases, focusing on one optimization technique; however, not all optimization techniques are suitable for a given problem. A limited number of studies have compared different techniques for a given problem under boundary conditions and constraints. This review revisits basic heat transfer problems and identifies a promising technique for each problem to minimize heat loss. The paper considers three techniques: nonlinear least-squares error (LSE), interior point linear programming (IPLP), and genetic algorithm. Two cases are studied: 1. heat loss optimization from cylindrical insulating surfaces and 2. laminar airflow on a heated plate. The results are compared for each technique, and a suitable technique is recommended for each considered case. Nonlinear LSE is found to be most suitable for case 1. IPLP and GA are recommended for the Case 2 problem. The average thermal conductivity is found to be 0.081 W/mK. The average insulation thickness is found to be 213.25 mm. This research will act as a basis for future research to justify and implement suitable techniques for different heat transfer problems.

"Freedom of design" in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties

The rational design of molecules with targeted quantum-mechanical (QM) properties requires an advanced understanding of the structure-property/property-property relationships (SPR/PPR) that exist across chemical compound space (CCS). In this work, we analyze these fundamental relationships in the sector of CCS spanned by small (primarily organic) molecules using the recently developed QM7-X dataset, a systematic, extensive, and tightly converged collection of 42 QM properties corresponding to ≈4.2M equilibrium and non-equilibrium molecular structures containing up to seven heavy/non-hydrogen atoms (including C, N, O, S, and Cl). By characterizing and enumerating progressively more complex manifolds of molecular property space-the corresponding high-dimensional space defined by the properties of each molecule in this sector of CCS-our analysis reveals that one has a substantial degree of flexibility or "freedom of design" when searching for a single molecule with a desired pair of properties or a set of distinct molecules sharing an array of properties. To explore how this intrinsic flexibility manifests in the molecular design process, we used multi-objective optimization to search for molecules with simultaneously large polarizabilities and HOMO-LUMO gaps; analysis of the resulting Pareto fronts identified non-trivial paths through CCS consisting of sequential structural and/or compositional changes that yield molecules with optimal combinations of these properties.

A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

An Overview of the Application of Harmony Search for Chemical Engineering Optimization

Harmony search algorithm and its variants have been used in several applications in medicine, telecommunications, computer science, and engineering. This article reviews the global and multi-objective optimization for chemical engineering using harmony search. The main features of the HS method and several of its popular variants and hybrid versions including their relevant algorithm characteristics are described and discussed. A variety of global and multi-objective optimization problems from chemical engineering and their resolution using HS-based methods are also included. These problems involve thermodynamic calculations (phase stability analysis, phase equilibrium calculations, parameter estimation, and azeotrope calculation), heat exchanger design, distillation simulation, life cycle analysis, and water distribution systems, among others. Remarks on future developments of HS and its related algorithms for global and multi-objective optimization in chemical engineering are also provided in this review. HS is a reliable and promising stochastic optimizer to resolve challenging global and multi-objective optimization problems for process systems engineering.

Economic Benefits of Waste Pickling Solution Valorization

An integrated hybrid membrane process, composed of a diffusion dialysis (DD), a membrane distillation (MD) and a reactive precipitation unit (CSTR), is proposed as a promising solution for the valorization and onsite recycling of pickling waste streams. An economic analysis was performed aiming to demonstrate the feasibility of the developed process with a NPV of about EUR 40,000 and a DPBP of 4 years. The investment and operating costs, as well as the avoided costs and the benefits for the company operating the plant, were analyzed with an extensive cost tracking exercise and through face-to-face contact with manufacturers and sector leaders. A mathematical model was implemented using the gPROMS modelling platform. It is able to simulate steady state operations and run optimization analysis of the process performance. The impact of key operating and design parameters, such as the set-point bath concentration and the DD and MD membrane areas, respectively, was investigated and the optimal arrangement was identified. Finally, operating variables and design parameters were optimized simultaneously in a nonlinear framework as a tradeoff between profitability and environmental impact. We show how the integration of new technologies into the traditional pickling industry could provide a significant benefit for the issues of process sustainability, which are currently pressing.

Multi-Objective Optimisation of Biodiesel Synthesis in Simulated Moving Bed Reactor

In this work, multi-objective optimisation study was performed to determine the performance improvement in a simulated moving bed reactor (SMBR) for biodiesel synthesis. The selection of the operating parameters such as switching time, liquid flow rates in various sections, as well as the length and number of columns is not straightforward in an SMBR. In most cases, conflicting requirements and constraints influence the optimal selection of the decision (operating or design) variables. A mathematical model that predicts single-column experimental results well was modified and verified experimentally for multiple-column SMBR system. In this article, a few multi-objective optimisation problems were carried out for both existing set-up as well as at the design stage. A non-dominated sorting genetic algorithm (NSGA) was used as the optimisation tool for the optimisation study. Due to conflicting effect of process parameters, the multi-objective optimisation study resulted in non-dominated Pareto optimal solutions. It was shown that significant increase in yield and purity of biodiesel in SMBR was possible both for operating and at design stage.

Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm(SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

Optimal Control Applied to Oenological Management of Red Wine Fermentative Macerations

The management of wineries for industrial red winemaking is limited by the capacity and availability of fermentation tanks over the harvest season. The winemakers aim to optimize the wine quality, the fermentative maceration length, and the fermentation tank’s productive cycle simultaneously. Maceration in varietal wine production is carried out until a specific sugar content (digging-out point) is attained, finishing before alcoholic fermentation. Winemakers have found that by trial and error handling of the digging-out point, they can improve the winery capacity and production cost. In this work, we develop an optimal control problem for managing the digging-out point considering two objectives associated with process efficiency and costs. A good compromise between these objectives was found by applying multi-criteria decision-making (MCDM) techniques and the knee point. Two control strategies were compared: free nutrition and traditional nutrition. TOPSIS and LINMAP algorithms were used to choose the most suitable strategy that coincided with the knee point. The preferred option was nitrogen addition only at the beginning of fermentation (6.6–10.6 g/hL of DAP) and a high fermentation temperature (30 °C), yielding the desired digging-out point with a small error (6–9 g/L).

Exploiting Trade-Off Criteria to Improve the Efficiency of Genetic Multi-Objective Optimisation Algorithms

The highly competitive nature of the chemical industry requires the optimisation of the design and exploitation of (bio-)chemical processes with respect to multiple, often conflicting objectives. Genetic algorithms are widely used in the context of multi-objective optimisation due to their overall straightforward implementation and numerous other advantages. NSGA-II, one of the current state-of-the-art algorithms in genetic multi-objective optimisation has, however, two major shortcomings, inherent to evolutionary algorithms: 1) the inability to distinguish between solutions based on their mutual trade-off and distribution; 2) a problem-irrelevant stopping criterion based on a maximum number of iterations. The former results in a Pareto front that contains redundant solutions. The latter results in an unnecessary high computation time. In this manuscript, a novel strategy is presented to overcome these shortcomings: t-domination. t-domination uses the concept of regions of practically insignificant trade-off (PIT-regions) to distinguish between solutions based on their trade-off. Two solutions that are located in each other’s PIT-regions are deemed insignificantly different and therefore one can be discarded. Additionally, extrapolating the concept of t-domination to two subsequent solution populations results in a problem-relevant stopping criterion. The novel algorithm is capable of generating a Pareto front with a trade-off-based solution resolution and displays a significant reduction in computation time in comparison to the original NSGA-II algorithm. The algorithm is illustrated on benchmark scalar case studies and a fed-batch reactor case study.

Multicriteria Optimization of Molecular Models of Water Using a Reduced Units Approach

Multicriteria optimization (MCO) is used to parametrize molecular models of water. The set of the best possible compromises between different objectives, the Pareto set, is determined. Calculating Pareto sets for optimization problems involving molecular simulations is computationally expensive. Therefore, we use a novel, highly efficient method, which is based on the fact that numerical results from molecular simulations can be interpreted as dimensionless numbers. Hence, they carry information on an entire class of models in physical units. This approach was applied here for the MCO of water models of the "one-center Lennard-Jones + point charge" type, in which the objectives were the quality of the description of the vapor pressure, liquid density, and enthalpy of vaporization. The results were compared to models from the literature. Significant improvements were observed. The new optimization method for the development of molecular models is efficient, robust, and broadly applicable.

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

This tutorial and review of multi-objective optimization (MOO) gives a detailed explanation of the 5 steps to create, solve, and then select the optimum result. Unlike single-objective optimization, the fifth step of selection or ranking of solutions is often overlooked by the authors of papers dealing with MOO applications. It is necessary to undertake a multi-criteria analysis to choose the best solution. A review of the recent publications using MOO for chemical process engineering problems shows a doubling of publications between 2016 and 2019. MOO applications in the energy area have seen a steady increase of over 20% annually over the last 10 years. The three key methods for solving MOO problems are presented in detail, and an emerging area of surrogate-assisted MOO is also described. The objectives used in MOO trade off conflicting requirements of a chemical engineering problem; these include fundamental criteria such as reaction yield or selectivity; economics; energy requirements; environmental performance; and process control. Typical objective functions in these categories are described, selection/ranking techniques are outlined, and available software for MOO are listed. It is concluded that MOO is gaining popularity as an important tool and is having an increasing use and impact in chemical process engineering.

Retooling Asymmetric Conjugate Additions for Sterically Demanding Substrates with an Iterative Data-Driven Approach

The development of catalytic enantioselective methods is routinely carried out using easily accessible and prototypical substrates. This approach to reaction development often yields asymmetric methods that perform poorly using substrates that are sterically or electronically dissimilar to those used during the reaction optimization campaign. Consequently, expanding the scope of previously optimized catalytic asymmetric reactions to include more challenging substrates is decidedly nontrivial. Here, we address this challenge through the development of a systematic workflow to broaden the applicability and reliability of asymmetric conjugate additions to substrates conventionally regarded as sterically and electronically demanding. The copper-catalyzed asymmetric conjugate addition of alkylzirconium nucleophiles to form tertiary centers, although successful for linear alkyl chains, fails for more sterically demanding linear α,β-unsaturated ketones. Key to adapting this method to obtain high enantioselectivity was the synthesis of modified phosphoramidite ligands, designed using quantitative structure–selectivity relationships (QSSRs). Iterative rounds of model construction and ligand synthesis were executed in parallel to evaluate the performance of 20 chiral ligands. The copper-catalyzed asymmetric addition is now more broadly applicable, even tolerating linear enones bearing tert-butyl β-substituents. The presence of common functional groups is tolerated in both nucleophiles and electrophiles, giving up to 99% yield and 95% ee across 20 examples.

Advances in Energy Systems Engineering and Process Systems Engineering in China—A Review Starting from Sargent’s Pioneering Work

Process systems engineering (PSE), after being proposed by Sargent and contemporary researchers, has been fast developing in various domains and research communities around the world in the last couple of decades, with energy systems engineering featuring a typical yet still fast propagating domain, and the Chinese PSE community featuring a typical community with its own unique challenges for applying PSE theory and methods. In this paper, development of energy systems engineering and process systems engineering in China is discussed, and Sargent’s impacts on these two fields are the main focus. Pioneering work conducted by Sargent is firstly discussed. Then, a venation on how his work and thoughts have motivated later researchers and led to progressive advances is reviewed and analyzed. It shows that Sargent’s idea of optimum design and his work on nonlinear programming and superstructure modelling have resulted in well-known methods that are widely adopted in energy systems engineering and PSE applications in tackling problems in China. Following Sargent’s pioneering ideas and conceptual design of the PSE mansion, future development directions of energy systems engineering are also discussed.

Data-driven multi-objective optimization via grid compatible simplex technique and desirability approach for challenging high throughput chromatography applications

Recently, a grid compatible Simplex variant has been demonstrated to identify optima consistently and rapidly in challenging high throughput (HT) applications in early bioprocess development. Here, this method is extended by deploying it to multi-objective optimization problems. Three HT chromatography case studies are presented, each posing challenging early development situations and including three responses which were amalgamated by the adoption of the desirability approach. The suitability of a design of experiments (DoE) methodology per case study, using regression analysis in addition to the desirability approach, was evaluated for a large number of weights and in the presence of stringent and lenient performance requirements. Despite the adoption of high-order models, this approach had low success in identification of the optimal conditions. For the deployment of the Simplex approach, the deterministic specification of the weights of the merged responses was avoided by including them as inputs in the formulated multi-objective optimization problem, facilitating this way the decision making process. This, and the ability of the Simplex method to locate optima, rendered the presented approach highly successful in delivering rapidly operating conditions, which belonged to the Pareto set and offered a superior and balanced performance across all outputs compared to alternatives. Moreover, its performance was relatively independent of the starting conditions and required sub-minute computations despite its higher order mathematical functionality compared to DoE techniques. These evidences support the suitability of the grid compatible Simplex method for early bioprocess development studies involving complex data trends over multiple responses. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1393-1406, 2018.

Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality

In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.

Dynamic optimization of biological networks under parametric uncertainty

BACKGROUND

Micro-organisms play an important role in various industrial sectors (including biochemical, food and pharmaceutical industries). A profound insight in the biochemical reactions inside micro-organisms enables an improved biochemical process control. Biological networks are an important tool in systems biology for incorporating microscopic level knowledge. Biochemical processes are typically dynamic and the cells have often more than one objective which are typically conflicting, e.g., minimizing the energy consumption while maximizing the production of a specific metabolite. Therefore multi-objective optimization is needed to compute trade-offs between those conflicting objectives. In model-based optimization, one of the inherent problems is the presence of uncertainty. In biological processes, this uncertainty can be present due to, e.g., inherent biological variability. Not taking this uncertainty into account, possibly leads to the violation of constraints and erroneous estimates of the actual objective function(s). To account for the variance in model predictions and compute a prediction interval, this uncertainty should be taken into account during process optimization. This leads to a challenging optimization problem under uncertainty, which requires a robustified solution.

RESULTS

Three techniques for uncertainty propagation: linearization, sigma points and polynomial chaos expansion, are compared for the dynamic optimization of biological networks under parametric uncertainty. These approaches are compared in two case studies: (i) a three-step linear pathway model in which the accumulation of intermediate metabolites has to be minimized and (ii) a glycolysis inspired network model in which a multi-objective optimization problem is considered, being the minimization of the enzymatic cost and the minimization of the end time before reaching a minimum extracellular metabolite concentration. A Monte Carlo simulation procedure has been applied for the assessment of the constraint violations. For the multi-objective case study one Pareto point has been considered for the assessment of the constraint violations. However, this analysis can be performed for any Pareto point.

CONCLUSIONS

The different uncertainty propagation strategies each offer a robustified solution under parametric uncertainty. When making the trade-off between computation time and the robustness of the obtained profiles, the sigma points and polynomial chaos expansion strategies score better in reducing the percentage of constraint violations. This has been investigated for a normal and a uniform parametric uncertainty distribution. The polynomial chaos expansion approach allows to directly take prior knowledge of the parametric uncertainty distribution into account.