Designing a simple, yet representative reaction network for subsequent micro-kinetic analysis is important for limiting the cost of evaluation and ensuring model solvability. This is currently achieved by employing sensitivity analysis over a comprehensive reaction network (CRN) to screen reaction species. However, as a reaction network is being simplified for a particular catalyst composition, it loses its transferability to other compositions. Therefore, in this study, a two-way approach is presented to circumvent this problem. Firstly, a generalizable model outcome is identified, i.e. minimum reactant conversions (x_R), based on a mass-flow analysis. Then, a stepwise workflow is developed for constructing a partial reaction network (PRN) to insure transferability of min (x_R) for a range of varying catalyst energetics, in the absence of experimental data for validation. Lastly, the transferability of this approach is demonstrated for CH₄ dry reforming by developing a PRN using Ni(1 1 1) as the initial catalyst and testing it over Ru(0 0 1).

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The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.

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Production of functional molecules from renewable bio-feedstocks and bio-waste has the potential to significantly reduce the greenhouse gas emissions. However, the development of such processes commonly requires invention and scale-up of highly selective and robust chemistry for complex reaction networks in bio-waste mixtures. We demonstrate an approach to optimising a chemical route for multiple objectives starting from a mixture derived from bio-waste. We optimise the recently developed route from a mixture of waste terpenes to p-cymene. In the first reaction step it was not feasible to build a detailed kinetic model. A Bayesian multiple objectives optimisation algorithm TS-EMO was used to optimise the first two steps of reaction for maximum conversion and selectivity. The model suggests a set of very different conditions that result in simultaneous high values of the two outputs.

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Novel materials are the backbone of major technological advances. However, the development and wide-scale introduction of new materials, such as nanomaterials, is limited by three main factors—the expense of experiments, inefficiency of synthesis methods and complexity of scale-up. Reaching the kilogram scale is a hurdle that takes years of effort for many nanomaterials. We introduce an improved methodology for materials development, combining state-of-the-art techniques—multi-objective machine learning optimization, high yield microreactors and high throughput analysis. We demonstrate this approach through the optimization of ZnO nanoparticle synthesis, simultaneously targeting high yield and high antibacterial activity. In fewer than 100 experiments, we developed a 1 kg day⁻¹ continuous synthesis for ZnO (with a space-time-yield of 62.4 kg day⁻¹ m⁻³), having an antibacterial activity comparable to hydrothermally synthesized nano-ZnO and cetrimonium bromide. Following this, we provide insights into the mechanistic factors underlying the performance-yield tradeoffs of synthesis and highlight the need for benchmarking machine learning models with traditional chemical engineering methods. Methods for increasing model accuracy at steep pareto fronts, in this case at yields close to 1 kg per day, should also be improved. To project the next steps for process scale-up and the potential advantages of this methodology, we conduct a scalability analysis in comparison to conventional batch production methods, in which there is a significant reduction in degrees of freedom. The proposed method has the potential to significantly reduce experimental costs, increase process efficiency and enhance material performance, which culminate to form a new pathway for materials discovery.

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Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multiobjective optimization of PSA is a challenging task. To tackle this, we propose a hybrid optimization framework (TSEMO + DyOS), which integrates two steps. In the first step, a Bayesian stochastic multiobjective optimization algorithm (i.e., TSEMO) searches the entire decision space and identifies an approximated Pareto front within a small number of simulations. Within TSEMO, Gaussian process (GP) surrogate models are trained to approximate the original full process models. In the second step, a gradient-based deterministic algorithm (i.e., DyOS) is initialized at the approximated Pareto front to further refine the solutions until local optimality. Therein, the full process model is used in the optimization. The proposed hybrid framework is efficient, because it benefits from the coarse-to-fine function evaluations and stochastic-to-deterministic searching strategy. When the result is far away from the optima, TSEMO can efficiently approximate a trade-off curve as good as a commonly used evolutional algorithm, i.e., Nondominated Sorting Genetic Algorithm II (NSGA-II), while TSEMO only uses around 1/16th of CPU time of NSGA-II. This is because the GP-based surrogate model is utilized for function evaluations in the initial coarse search. When the result is near the optima, the searching efficiency of TSEMO dramatically decreases, while DyOS can accelerate the searching efficiency by over 10 times. This is because, in the proximity of optima, the exploitation capacity of DyOS is significantly higher than that of TSEMO.

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This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data. The chemical industry requires a transformation towards more sustainable practices, eliminating its dependencies on fossil fuels and limiting its impact on the environment. However, identifying more sustainable process alternatives is, at present, a cumbersome, manual, iterative process, based on chemical intuition and modelling. We give a perspective on methods for automated discovery and assessment of competitive sustainable reaction routes based on renewable or waste feedstocks. Three key areas of transition are outlined and reviewed based on their state-of-the-art as well as bottlenecks: (i) data, (ii) evaluation metrics, and (iii) decision-making. We elucidate their synergies and interfaces since only together these areas can bring about the most benefit. The field of chemical data intelligence offers the opportunity to identify the inherently more sustainable reaction pathways and to identify opportunities for a circular chemical economy. Our review shows that at present the field of data brings about most bottlenecks, such as data completion and data linkage, but also offers the principal opportunity for advancement.

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New material innovation is limited by the time, expertise and cost of development. In the face of rapidly growing crises like pandemics, resource scarcity and climate change, we require new methods and methodologies to create and scale-up new technologies. In this work, we introduce an accelerated platform for material development, featuringadvanced machine learning methods, continuous microreactors and automation. As a case study, this platform was used to develop highly antimicrobial zinc oxide materials for coatings, with simultaneously optimized yield and performance. Continuous, high-shear microreactors and tangential flow filtration were employed at the lab-scale to enhance space time yields and scalability via number-up and scale-out, and culminating in ~ kg/day scale production.Decision-making was accelerated with the use of a multi-objective optimization algorithm for experimental design,allowing fast exploration of many variables without lengthy factorial designs and heuristic scale-up techniques.Furthermore, this platform was used to probe relationships between material characteristics (such as morphology and crystallinity), application performance and process parameters, showcasing the capability of this platform to make new discoveries and mechanistic insights. This approach features broad applicability to a range of materials and industries,including energy storage, pharmaceuticals and catalysis.@en

Optimal reaction pathways for the conversion of renewable feedstocks are often examined by reaction network flux analysis. An alternative modelling approach for reaction networks is a Petri net. These explicitly take the reaction sequence into account. In the optimisation of a network, this can allow the implementation of constraints on circular reaction structures, which are common substructures in chemical reaction networks. In this study, we compare the performance of the models in an illustrative minimal working example of a circular reaction substructure. The reaction network flux analysis is shown to be a relaxation of the Petri net formulation. Most notable, this work contributes to well-reasoned model choices for reaction networks.

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Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)₂(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives-high conversion and high diastereomeric excess-the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.

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Many engineering problems require the optimization of expensive, black-box functions involving multiple conflicting criteria, such that commonly used methods like multiobjective genetic algorithms are inadequate. To tackle this problem several algorithms have been developed using surrogates. However, these often have disadvantages such as the requirement of a priori knowledge of the output functions or exponentially scaling computational cost with respect to the number of objectives. In this paper a new algorithm is proposed, TSEMO, which uses Gaussian processes as surrogates. The Gaussian processes are sampled using spectral sampling techniques to make use of Thompson sampling in conjunction with the hypervolume quality indicator and NSGA-II to choose a new evaluation point at each iteration. The reference point required for the hypervolume calculation is estimated within TSEMO. Further, a simple extension was proposed to carry out batch-sequential design. TSEMO was compared to ParEGO, an expected hypervolume implementation, and NSGA-II on nine test problems with a budget of 150 function evaluations. Overall, TSEMO shows promising performance, while giving a simple algorithm without the requirement of a priori knowledge, reduced hypervolume calculations to approach linear scaling with respect to the number of objectives, the capacity to handle noise and lastly the ability for batch-sequential usage.

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This paper explores the feasibility of controlling the selectivity of a partial oxidation reaction by simultaneous modulation of local oxygen concentration and coolant temperature along the length of a reactor. The microstructured membrane reactor (MMR) concept consists of an oxygen-permeable membrane for distributing the oxygen feed with the coolant channel divided into zones of different temperatures. The reactor concept was explored in simulation of the selective oxidation of o-xylene to phthalic anhydride (PA). A mathematical model of the reactor was developed and optimization performed with the objective of maximising PA selectivity at the reactor outlet. Dosing of oxygen at uniform wall temperature results in PA selectivity increase by 6.3%, albeit with a reduction in o-xylene conversion of about 8% compared to a conventional fixed bed reactor. However, simultaneous modulation of both reactor wall temperatures and local oxygen concentration results in an improved conversion of o-xylene without a detrimental effect on selectivity, thus giving maximum yield of PA.

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Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance criteria. Herein we report the implementation of a new multi-objective machine learning optimization algorithm for self-optimization, and demonstrate it in two exemplar chemical reactions performed in continuous flow. The algorithm successfully identified a set of optimal conditions corresponding to the trade-off curve (Pareto front) between environmental and economic objectives in both cases. Thus, it reveals the complete underlying trade-off and is not limited to one compromise as is the case in many other studies. The machine learning algorithm proved to be extremely data efficient, identifying the optimal conditions for the objectives in a lower number of experiments compared to single-objective optimizations. The complete underlying trade-off between multiple objectives is identified without arbitrary weighting factors, but via true multi-objective optimization.

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A decision support tool has been developed that uses global multiobjective optimization based on 1) the environmental impacts, evaluated within the framework of full life cycle assessment; and 2) process costs, evaluated by using rigorous process models. This approach is particularly useful in developing biorenewable-based energy solutions and chemicals manufacturing, for which multiple criteria must be evaluated and optimization-based decision-making processes are particularly attractive. The framework is demonstrated by using a case study of the conversion of terpenes derived from biowaste feedstocks into reactive intermediates. A two-step chemical conversion/separation sequence was implemented as a rigorous process model and combined with a life cycle model. A life cycle inventory for crude sulfate turpentine was developed, as well as a conceptual process of its separation into pure terpene feedstocks. The performed single- and multiobjective optimizations demonstrate the functionality of the optimization-based process development and illustrate the approach. The most significant advance is the ability to perform multiobjective global optimization, resulting in identification of a region of Pareto-optimal solutions.

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