Design of computer experiments: A review

Computational-Aided Approach for the Optimization of Microfluidic-Based Nanoparticles Manufacturing Process

In the last few years, the microfluidic production of nanoparticles (NPs) is becoming a promising alternative to conventional industrial approaches (e.g., nanoprecipitation, salting out, and emulsification-diffusion) thanks to the production efficiency, low variability, and high controllability of the production parameters. Nevertheless, the development of new formulations and the switching of the production process toward microfluidic platforms requires expensive and time-consuming number of experiments for the tuning of the formulation to obtain NPs with specific morphological and functional characteristics. In this work, we developed a computational fluid dynamic pipeline, validated through an ad hoc experimental strategy, to reproduce the mixing between the solvent and anti-solvent (i.e., acetonitrile and TRIS-HCl, respectively). Moreover, beyond the classical variables able to describe the mixing performances of the microfluidic chip, novel variables were described in order to assess the region of the NPs formation and the changing of the amplitude of the precipitation region according to different hydraulic conditions. The numerical approach proved to be able to capture a progressive reduction of the nanoprecipitation region due to an increment of the flow rate ratio; in parallel, through the experimental production, a progressive increment of the NPs size heterogeneity was observed with the same fluid dynamic conditions. Hence, the preliminary comparison between numerical and experimental evidence proved the effectiveness of the computational strategy to optimize the NPs manufacturing process.

A dynamic knowledge graph approach to distributed self-driving laboratories

The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

Optimization of a Screw Centrifugal Blood Pump Based on Random Forest and Multi-Objective Gray Wolf Optimization Algorithm

The centrifugal blood pump is a commonly used ventricular assist device. It can replace part of the heart function, pumping blood throughout the body in order to maintain normal function. However, the high shear stress caused by the impeller rotating at high speeds can lead to hemolysis and, as a consequence, to stroke and other syndromes. Therefore, reducing the hemolysis level while ensuring adequate pressure generation is key to the optimization of centrifugal blood pumps. In this study, a screw centrifugal blood pump was used as the research object. In addition, pressure generation and the hemolysis level were optimized simultaneously using a coupled algorithm composed of random forest (RF) and multi-objective gray wolf optimization (MOGWO). After verifying the prediction accuracy of the algorithm, three optimized models were selected and compared with the baseline model in terms of pressure cloud, 2D streamline, SSS distribution, HI distribution, and vortex distribution. Finally, via a comprehensive evaluation, the optimized model was selected as the final optimization design, in which the pressure generation increased by 24% and the hemolysis value decreased by 48%.

A low dimensional surrogate model for a fast estimation of strain in the thrombus during a thrombectomy procedure

BACKGROUND

Intra-arterial thrombectomy is the main treatment for acute ischemic stroke due to large vessel occlusions and can consist in mechanically removing the thrombus with a stent-retriever. A cause of failure of the procedure is the fragmentation of the thrombus and formation of micro-emboli, difficult to remove. This work proposes a methodology for the creation of a low-dimensional surrogate model of the mechanical thrombectomy procedure, trained on realizations from high-fidelity simulations, able to estimate the evolution of the maximum first principal strain in the thrombus.

METHOD

A parametric finite-element model was created, composed of a tapered vessel, a thrombus, a stent-retriever and a catheter. A design of experiments was conducted to sample 100 combinations of the model parameters and the corresponding thrombectomy simulations were run and post-processed to extract the maximum first principal strain in the thrombus during the procedure. Then, a surrogate model was built with a combination of principal component analysis and Kriging.

RESULTS

The surrogate model was chosen after a sensitivity analysis on the number of principal components and was tested with 10 additional cases. The model provided predictions of the strain curves with correlation above 0.9 and a maximum error of 28%, with an error below 20% in 60% of the test cases.

CONCLUSIONS

The surrogate model provides nearly instantaneous estimates and constitutes a valuable tool for evaluating the risk of thrombus rupture during pre-operative planning for the treatment of acute ischemic stroke.

Predicting the Optimal Input Parameters for the Desired Print Quality Using Machine Learning

3D printing is a growing technology being incorporated into almost every industry. Although it has obvious advantages, such as precision and less fabrication time, it has many shortcomings. Although several attempts were made to monitor the errors, many have not been able to thoroughly address them, like stringing, over-extrusion, layer shifting, and overheating. This paper proposes a study using machine learning to identify the optimal process parameters such as infill structure and density, material (ABS, PLA, Nylon, PVA, and PETG), wall and layer thickness, count, and temperature. The result thus obtained was used to train a machine learning algorithm. Four different network architectures (CNN, Resnet152, MobileNet, and Inception V3) were used to build the algorithm. The algorithm was able to predict the parameters for a given requirement. It was also able to detect any errors. The algorithm was trained to pause the print immediately in case of a mistake. Upon comparison, it was found that the algorithm built with Inception V3 achieved the best accuracy of 97%. The applications include saving the material from being wasted due to print time errors in the manufacturing industry.

Ammonia quantum tunneling in cold rare-gas He and Ar clusters and factorial design approach for methodology evaluation

Quantum tunneling of the ammonia inversion motion and energy level splittings in He and Ar clusters were investigated. It was found that the double well potential (DWP) in He clusters is symmetrical and that the first layer of He atoms is able to model the system. The calculated tunneling splitting was in good agreement with the experimental, 36.4 and 24.6 cm[Formula: see text] respectively. For NH[Formula: see text] in Ar clusters, the DWP becomes slightly asymmetric, which is enough to decrease the resonance and make the symmetric DWP unable to model the system. An asymmetric potential was used and the result was in excellent agreement with the experimental splitting, of 9.0 and 10.6 cm[Formula: see text] respectively. Non-covalent interactions revealed that the asymmetry is caused by dissimilar interactions in each minimum of the double well potential. The effects of different methodologies were analyzed via a design of experiments approach. For the gas-phase NH[Formula: see text] molecule, only diffuse functions were statistically significant while for the NH[Formula: see text] embedded in He cluster both the MP2 method and polarization functions were significant. This tendency suggests higher order polarization functions may be essential to generate accurate barrier heights.

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.

Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation

Surrogate-model-assisted uncertainty treatment practices have been the subject of increasing attention and investigations in recent decades for many symmetrical engineering systems. This paper delivers a review of surrogate modeling methods in both uncertainty quantification and propagation scenarios. To this end, the mathematical models for uncertainty quantification are firstly reviewed, and theories and advances on probabilistic, non-probabilistic and hybrid ones are discussed. Subsequently, numerical methods for uncertainty propagation are broadly reviewed under different computational strategies. Thirdly, several popular single surrogate models and novel hybrid techniques are reviewed, together with some general criteria for accuracy evaluation. In addition, sample generation techniques to improve the accuracy of surrogate models are discussed for both static sampling and its adaptive version. Finally, closing remarks are provided and future prospects are suggested.

From Platform to Knowledge Graph: Evolution of Laboratory Automation

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

Anti-Caking Coatings for Improving the Useful Properties of Ammonium Nitrate Fertilizers with Composition Modeling Using Box-Behnken Design

Granular fertilizers (especially those based on ammonium nitrate (AN)) tend to agglomerate during storage. The aims of this research were to develop effective anti-caking coatings for ammonium nitrate fertilizers while improving the quality of fertilizers and to optimize the composition of effective anti-caking coatings. The influence of the composition of the prepared organic coatings on the effectiveness of preventing the caking of fertilizers was studied by response surface methodology (RSM) using Box–Behnken design (BBD). Additionally, the effect of the developed anti-caking agents on the quality of fertilizers was determined by measuring the crushing strength of the granules. The prepared coatings included fatty amine, stearic acid, surfactant, and paraffin wax. Gas chromatography–mass spectrometry (GC–MS) was used to analyze these coatings. The morphology of the fertilizers were examined by scanning electron microscopy (SEM). Composition studies, based on statistical assessment, showed the coating components had a varying influence on preventing the caking of fertilizers after granulation and after 30 days of storage. The results demonstrated that increasing the content of fatty amines and reducing surfactant in the composition of coating had positive effects on caking prevention. In this study, more effective and economically viable anti-caking coatings were developed. In addition, the present work could serve as a basis to further improve anti-caking coatings.

Determining usefulness of machine learning in materials discovery using simulated research landscapes

When existing experimental data are combined with machine learning (ML) to predict the performance of new materials, the data acquisition bias determines ML usefulness and the prediction accuracy. In this context, the following two conditions are highly common: (i) constructing new unbiased data sets is too expensive and the global knowledge effectively does not change by performing a limited number of novel measurements; (ii) the performance of the material depends on a limited number of physical parameters, much smaller than the range of variables that can be changed, albeit such parameters are unknown or not measurable. To determine the usefulness of ML under these conditions, we introduce the concept of simulated research landscapes, which describe how datasets of arbitrary complexity evolve over time. Simulated research landscapes allow us to use different discovery strategies to compare standard materials exploration with ML-guided explorations, i.e. we can measure quantitatively the benefit of using a specific ML model. We show that there is a window of opportunity to obtain a significant benefit from ML-guided strategies. The adoption of ML can take place too soon (not enough information to find patterns) or too late (dense datasets only allow for negligible ML benefit), and the adoption of ML can even slow down the discovery process in some cases. We offer a qualitative guide on when ML can accelerate the discovery of new best-performing materials in a field under specific conditions. The answer in each case depends on factors like data dimensionality, corrugation and data collection strategy. We consider how these factors may affect the ML prediction capabilities and discuss some general trends.

Effect of Blade Leading and Trailing Edge Configurations on the Performance of a Micro Tubular Propeller Turbine Using Response Surface Methodology

With the recent rise in importance of environmental issues, research on micro hydropower, a kind of renewable energy source, is being actively conducted. In this study, a micro tubular propeller turbine was selected for study of micro hydropower in pipes. Numerical analysis was conducted to evaluate the performance. Response surface methodology using design of experiments was performed to efficiently investigate the effect of the blade leading and trailing edge elliptic aspect ratios on the performance. The trailing edge configuration was found to be more related to the performance, because of the drastic pressure variation due to the stagnation point formed, regardless of the leading edge configuration. To improve the performance, a NACA airfoil was introduced. The results show that the flow became more stable than the reference model, and the efficiency was increased by 2.44%.

Using Metamodeling to Identify the Optimal Strategy for Colorectal Cancer Screening

OBJECTIVES

Metamodeling can address computational challenges within decision-analytic modeling studies evaluating many strategies. This article illustrates the value of metamodeling for evaluating colorectal cancer screening strategies while accounting for colonoscopy capacity constraints.

METHODS

In a traditional approach, the best screening strategy was identified from a limited subset of strategies evaluated with the validated Adenoma and Serrated pathway to Colorectal CAncer model. In a metamodeling approach, metamodels were fitted to this limited subset to evaluate all potentially plausible strategies and determine the best overall screening strategy. Approaches were compared based on the best screening strategy in life-years gained compared with no screening. Metamodel runtime and accuracy was assessed.

RESULTS

The metamodeling approach evaluated >40 000 strategies in <1 minute with high accuracy after 1 adaptive sampling step (mean absolute error: 0.0002 life-years) using 300 samples in total (generation time: 8 days). Findings indicated that health outcomes could be improved without requiring additional colonoscopy capacity. Obtaining similar insights using the traditional approach could require at least 1000 samples (generation time: 28 days). Suggested benefits from screening at ages <40 years require adequate validation of the underlying Adenoma and Serrated pathway to Colorectal CAncer model before making policy recommendations.

CONCLUSIONS

Metamodeling allows rapid assessment of a vast set of strategies, which may lead to identification of more favorable strategies compared to a traditional approach. Nevertheless, metamodel validation and identifying extrapolation beyond the support of the original decision-analytic model are critical to the interpretation of results. The screening strategies identified with metamodeling support ongoing discussions on decreasing the starting age of colorectal cancer screening.

Towards the Development of Digital Twins for the Bio-manufacturing Industry

The bio-manufacturing industry, along with other process industries, now has the opportunity to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully accomplish this, a physical-to-digital-to-physical information loop should be carefully developed. One way to achieve this is, for example, through the implementation of digital twins (DTs), which are virtual copies of the processes. Therefore, in this paper, the focus is on understanding the needs and challenges faced by the bio-manufacturing industry when dealing with this digitalized paradigm. To do so, two major building blocks of a DT, data and models, are highlighted and discussed. Hence, firstly, data and their characteristics and collection strategies are examined as well as new methods and tools for data processing. Secondly, modelling approaches and their potential of being used in DTs are reviewed. Finally, we share our vision with regard to the use of DTs in the bio-manufacturing industry aiming at bringing the DT a step closer to its full potential and realization.

A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities

Support Vector Machines (SVMs) based optimization framework is presented for the data-driven optimization of numerically infeasible Differential Algebraic Equations (DAEs) without the full discretization of the underlying first-principles model. By formulating the stability constraint of the numerical integration of a DAE system as a supervised classification problem, we are able to demonstrate that SVMs can accurately map the boundary of numerical infeasibility. The necessity of this data-driven approach is demonstrated on a 2-dimensional motivating example, where highly accurate SVM models are trained, validated, and tested using the data collected from the numerical integration of DAEs. Furthermore, this methodology is extended and tested for a multi-dimensional case study from reaction engineering (i.e., thermal cracking of natural gas liquids). The data-driven optimization of this complex case study is explored through integrating the SVM models with a constrained global grey-box optimization algorithm, namely the ARGONAUT framework.

Sonohydrolysis using an enzymatic cocktail in the preparation of free fatty acid

In this work, the concept of lipase cocktail has been proposed in the ultrasound-assisted hydrolysis of coconut oil. Lipase from Thermomyces lanuginosus (TLL), lipase from Rhizomucor miehei (RML), and lipase B from Candida antarctica (CALB) were evaluated as biocatalysts in different combinations. The best conversion (33.66%) was achieved using only RML; however, the best lipase cocktail (75% RML and 25% CALB) proposed by the triangular response surface was used to achieve higher conversions. At the best lipase cocktail, reaction parameters [temperature, biocatalyst content and molar ratio (water/oil)] were optimized by a Central Composite Design, allowing to obtain more than 98% of conversion in the hydrolysis of coconut oil in 3 h of incubation at 37 kHz, 300 W and 45 °C by using 20% of the lipase cocktail (w/w) and a molar ratio of 7.5:1 (water/oil). The lipase cocktail retained about 50% of its initial activity after three consecutive cycles of hydrolysis. To the authors' knowledge, up to date, this communication is the first report in the literature for the ultrasound-assisted hydrolysis of coconut oil catalyzed by a cocktail of lipases. Under ultrasound irradiation, the concept of lipase cocktail was successfully applied, and this strategy could be useful for the other types of reactions using heterogeneous substrates.

Introduction to Metamodeling for Reducing Computational Burden of Advanced Analyses with Health Economic Models: A Structured Overview of Metamodeling Methods in a 6-Step Application Process

Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, although applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this article introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non-health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics: 1) the identification of a suitable metamodeling technique, 2) simulation of data sets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conducting the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed toward using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses (e.g., value of information analysis) with computationally burdensome simulation models.

Modeling buffer capacity and pH in acid and acidified foods

Standard ionic equilibria equations may be used for calculating pH of weak acid and base solutions. These calculations are difficult or impossible to solve analytically for foods that include many unknown buffering components, making pH prediction in these systems impractical. We combined buffer capacity (BC) models with a pH prediction algorithm to allow pH prediction in complex food matrices from BC data. Numerical models were developed using Matlab software to estimate the pH and buffering components for mixtures of weak acid and base solutions. The pH model was validated with laboratory solutions of acetic or citric acids with ammonia, in combinations with varying salts using Latin hypercube designs. Linear regressions of observed versus predicted pH values based on the concentration and pK values of the solution components resulted in estimated slopes between 0.96 and 1.01 with and without added salts. BC models were generated from titration curves for 0.6 M acetic acid or 12.4 mM citric acid resulting in acid concentration and pK estimates. Predicted pH values from these estimates were within 0.11 pH units of the measured pH. Acetic acid concentration measurements based on the model were within 6% accuracy compared to high-performance liquid chromatography measurements for concentrations less than 400 mM, although they were underestimated above that. The models may have application for use in determining the BC of food ingredients with unknown buffering components. Predicting pH changes for food ingredients using these models may be useful for regulatory purposes with acid or acidified foods and for product development. PRACTICAL APPLICATION: Buffer capacity models may benefit regulatory agencies and manufacturers of acid and acidified foods to determine pH stability (below pH 4.6) and how low-acid food ingredients may affect the safety of these foods. Predicting pH for solutions with known or unknown buffering components was based on titration data and models that use only monoprotic weak acids and bases. These models may be useful for product development and food safety by estimating pH and buffering capacity.

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

Multiphase systems are important in minerals processing, and usually include solid–solid and solid–fluid systems, such as in wet grinding, flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization in multiphase systems, are analyzed. Different scales of size and time are included, and therefore, the analysis includes modeling at the molecular level (molecular dynamic modeling) and unit operation level (e.g., computational fluid dynamic, CFD), and the application of optimization for the design of a plant. New strategies for the modeling, design, and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The integration of different modeling techniques such as CFD with discrete element simulation (DEM) and response surface methodology (RSM) with artificial neural networks (ANN) is included. The paper finishes with tools to study the uncertainty, both epistemic and stochastic, based on uncertainty and global sensitivity analyses, which is present in all mineral processing operations. It is shown that all of these areas are very active and can help in the understanding, operation, design, and optimization of mineral processing that involves multiphase systems. Future needs, such as meso-scale modeling, are highlighted.