Model-based Optimization Strategy Research Articles

Model-Based Optimization Strategy for Intensification in the Chromatographic Purification of Oligonucleotides

Model-Based Optimization Strategy for Intensification in the Chromatographic Purification of Oligonucleotides

Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.

Surrogate-based multi-objective design optimization of tree-shaped fins with uniform branch end distribution for latent heat thermal energy storage

The enhancement of latent heat thermal energy storage (LHTES) systems through fin geometry optimization remains a critical challenge for leveraging the full potential of renewable energy sources. This study focuses on optimizing the geometries of tree-shaped fins to enhance power and energy densities in LHTES systems. The goal is to find branch designs with high energy and power density through a novel surrogate model-based optimization strategy that explores a broad design space. The surrogate models applied, including linear regression, principal component analysis-based linear regression, artificial neural networks, and random forest, are evaluated for their predictive performance. The random forest model demonstrates superior accuracy in predicting targets. The optimization process results in a Pareto-optimal design with a volume fraction of 33.9%. This optimal design substantially enhances the system's power density by 61.6% compared to conventional plate fins at an equivalent energy density. This optimized design improves energy and power density, achieving a uniform end-to-branch distribution, which is a pivotal factor for consistent temperature distribution and improved thermal efficiency. By integrating surrogate-based optimization with broad ranges of the tree-shaped fin design, this research has significantly improved the operational efficiency of LHTES systems. This research promises more effective thermal management and provides a methodological framework for design innovation in thermal energy storage.

High-precision Lamb wave evaluation for corrosion damage with model-based Bayesian Optimization and Gaussian meta-modeling strategy

The presence of corrosion damage in metallic structures is a critical problem affecting system safety, which poses challenges for structural maintenance. Ultrasonic Lamb wave testing has shown many benefits, including high sensitivity and a large coverage area. However, multiple external factors are causing a mismatch between the imaging result and the actual damage. This study proposes a Bayesian Optimization-based model calibration framework, incorporating a Gaussian process (GP) measurement model, for accurate corrosion damage quantification. In this method, one critical problem is obtaining the high-fidelity Finite Element Method (FEM) model. The initial FEM model usually has inevitable modeling errors due to necessary simplifications and idealized constraints. Here we introduced Bayesian Optimization by which the numerical model could be calibrated. The calibrated FEM model, which had fewer discrepancies with actual experiments, could generate high-fidelity signals corresponding to various damage severity. Then, the GP measurement model outputted the mean and variance of the corrosion width and depth corresponding to the differences between experiment signals and model outputs. The inversion of damage information from the established GP model was accomplished by the genetic algorithm. Hence, both the damage size and depth could be evaluated with high accuracy. Once the corrosion information along different propagation paths was obtained, it can be combined with the location of the corrosion damage center for locating the edge points of the corrosion damage. In the end, the edge points were fitted using the least square algorithm. Validations were experimentally performed, and the detection result showed good consistency with the actual damage.

Management strategy of granular sludge settleability in saline denitrification: Insights from machine learning

The settleability of granular sludge is a crucial factor in maintaining an adequate amount of granules in the wastewater treatment process (WWTP). However, Accurate measurement of settleability is challenging due to the complex interactions in WWTP. To address this issue, an intelligent strategy for controlling the settleability of granular sludge is required. This study developed a machine learning (ML)-based model to predict the sludge volume index (SVI30). Three ML models, namely artificial neural networks (ANNs), random forest (RF), and support vector machine (SVM) models, were employed with an ANNs-based mixed liquor suspended solids (MLSS) soft sensor, which served as input data for predicting SVI30 using ML. The combination of the soft sensor’s output and ANNs yielded the highest performance with R2 and mean absolute error (MAE) of 0.8946 and 5.5 mL/g, respectively. The Shapely Additive Explanations (SHAP) analysis revealed that MLSS, salinity, glucose loading rate, and pH were significant factors influencing SVI30. The surrogate model-based optimization strategy of SVI30 was conducted using core control parameters (CCPs), namely glucose loading rate, effluent pH, and salinity. It was suggested that a glucose loading rate over 3.0 kg-N/m3-d and an effluent pH exceeding 9.0 can deteriorate the SVI30. To the best of our knowledge, this is the first study on the surrogate model-based suggestion of the management strategy in the granular sludge process under saline denitrification conditions.

Accelerated Lifetime Model-Based Design Optimization Strategy With Efficiency, Reliability, and Cost Trade-Off for High-Power Modular AFE Rectifiers

Accelerated Lifetime Model-Based Design Optimization Strategy With Efficiency, Reliability, and Cost Trade-Off for High-Power Modular AFE Rectifiers

An Integrated Co-Design Optimization Toolchain Applied to a Conjugate Cam-Follower Drivetrain System

Due to ever increasing performance requirements, model-based optimization and control strategies are increasingly being adopted by machine builders and automotive companies. However, this demands an increase in modelling effort and a growing knowledge of optimization techniques, as a sufficient level of detail is required in order to evaluate certain performance characteristics. Modelling tools such as MATLAB Simscape have been created to reduce this modelling effort, allowing for greater model complexity and fidelity. Unfortunately, this tool cannot be used with high-performance gradient-based optimization algorithms due to obfuscation of the underlying model equations. In this work, an optimization toolchain is presented that efficiently interfaces with MATLAB Simscape to reduce user effort and the necessary skill and computation time required for the optimization of high-fidelity drivetrain models. The toolchain is illustrated on an industrially relevant conjugate cam-follower system, which is modelled in the Simscape environment and validated with respect to a higher-fidelity modeling technique, namely, the finite element method (FEM).

A model-based optimization strategy to achieve fast and robust freeze-drying cycles

Freeze-drying is a time and cost-intensive process. The primary drying phase is the main target in a process optimization exercise. Biopharmaceuticals require an amorphous matrix for stabilization, which may collapse during primary drying if the critical temperature of the formulation is exceeded. The risk of product collapse should be minimized during a process optimization to accomplish a robust process, while achieving an economical process time. Mechanistic models facilitate the search for an optimal primary drying protocol. We propose a novel two-stage shelf temperature optimization approach to maximize sublimation during the primary drying phase, without risking product collapse. The approach includes experiments to obtain high-resolution variability data of process parameters such as the heat transfer coefficient, vial dimensions and dried layer resistance. These process parameters variability data are incorporated into an uncertainty analysis to estimate the risk of failure of the protocol. This optimization approach enables to identify primary drying protocols that are faster and more robust than a classical approach. The methodology was experimentally verified using two formulations which allow for either aggressive or conservative freeze-drying of biopharmaceuticals.

Model-based optimal management strategies to mitigate soil acidification and minimize nutrient losses for croplands

Model-based optimal management strategies to mitigate soil acidification and minimize nutrient losses for croplands

Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model

As a novel analytical method based on big data, machine learning model can explore the relationship between different parameters and draw universal conclusions, which was used to predict composting maturity and identify key parameters in this study. The results showed that the Stacking model exhibited excellent prediction accuracy. SHapley Additive exPlanations (SHAP) and Partial Dependence Analysis (PDA) were performed to evaluate the importance of different parameters as well as their optimal interval. Optimal starting conditions should be maintained in the mesophilic state (temperature: 30-45℃, moisture content: 55–65%, pH: 6.3–8.0), and nutrients (total nitrogen > 2.3%, total organic carbon > 35%) should be adjusted in the thermophilic state. Experiments revealed that model-based optimization strategies could improve composting maturity because they could optimize compost microbial flora and perform complex carbon cycle functions. In conclusion, this study provides new insights into the enhancement of the composting process.

Sensitivity Analysis Based NVH Optimization in Permanent Magnet Synchronous Machines Using Lumped Unit Force Response

Acoustic noise and vibration sources in electric machines are mainly of electromagnetic and structural origin. A lumped structural unit response-based sensitivity analysis procedure is proposed in this article, which isolates electromagnetic and structural impacts brought by variation of different design parameters in a fractional slot concentrated winding, surface mounted permanent magnet synchronous machine (SPMSM). The impact of ten design parameters on average torque, torque ripple, synchronous inductance, dominant spatial-temporal order airgap force, cogging torque, friction torque, dominant mode frequency and structural unit response is studied in detail for a 12 slot/10 pole (12s10p) SPMSM. Analysis reveals that on a 12s10p SPMSM, slot opening has a very high impact on dominant airgap force component. A multilevel nonlinear regression model-based fast optimization strategy is introduced considering electromagnetic and structural design objectives and constraints following the sensitivity analysis. The optimized design is prototyped to validate the proposed design approach. Impact hammer-based modal analysis, no load, load and run-up tests are performed on the prototype for experimental validation. Ranging from structural tests to electromagnetic tests, the experimental results closely follow and validate the simulation-based predictions.

Comparison of Simple Feedback Control Structures for Constrained Optimal Operation

Optimal operation of systems subject to constraints can be challenging, especially when the set of active constraints changes during operation due to disturbances. The use of traditional model-based real-time optimization strategies has limitations related to model-plant mismatch and computational effort, and therefore the use of simple feedback strategies in lower layers is a good alternative to avoid these issues on fast timescales. This work aims to evaluate the viability of region-based control structures using selectors and the use of a primal-dual feedback optimizing control framework on this kind of problem, through the study of two process systems with changing active constraints. While region-based strategies focus on the effective control in specific active constraint regions, the primal-dual approach allows for control in all possible regions, with the introduction of intermediate variables that estimate the Lagrange multiplier values. In the first case study, the traditional region-based strategy could not handle all constraint regions, and an additional logical switching was necessary to account for the remaining region. The implementation of primal-dual feedback optimizing control was flexible enough to control the system in all regions without the need for additional logic. The second case study presents more constraints than the first and increased nonlinearities, which makes finding controlled variables for the unconstrained degrees of freedom challenging. The primal-dual control framework was able to drive the system to the optimum in all considered regions. Therefore, this framework is deemed as a promising control structure for optimal operation in the presence of changing active constraints.

Optimisation of microalgal cultivation via nutrient-enhanced strategies: the biorefinery paradigm

BackgroundThe production of microalgal biofuels, despite their sustainable and renowned potential, is not yet cost-effective compared to current conventional fuel technologies. However, the biorefinery concept increases the prospects of microalgal biomass as an economically viable feedstock suitable for the co-production of multiple biofuels along with value-added chemicals. To integrate biofuels production within the framework of a microalgae biorefinery, it is not only necessary to exploit multi-product platforms, but also to identify optimal microalgal cultivation strategies maximising the microalgal metabolites from which biofuels are obtained: starch and lipids. Whilst nutrient limitation is widely known for increasing starch and lipid formation, this cultivation strategy can greatly reduce microalgal growth. This work presents an optimisation framework combining predictive modelling and experimental methodologies to effectively simulate and predict microalgal growth dynamics and identify optimal cultivation strategies.ResultsMicroalgal cultivation strategies for maximised starch and lipid formation were successfully established by developing a multi-parametric kinetic model suitable for the prediction of mixotrophic microalgal growth dynamics co-limited by nitrogen and phosphorus. The model’s high predictive capacity was experimentally validated against various datasets obtained from laboratory-scale cultures of Chlamydomonas reinhardtii CCAP 11/32C subject to different initial nutrient regimes. The identified model-based optimal cultivation strategies were further validated experimentally and yielded significant increases in starch (+ 270%) and lipid (+ 74%) production against a non-optimised strategy.ConclusionsThe optimised microalgal cultivation scenarios for maximised starch and lipids, as identified by the kinetic model presented here, highlight the benefits of exploiting modelling frameworks as optimisation tools that facilitate the development and commercialisation of microalgae-to-fuel technologies.

Optimization by moving ridge functions: derivative-free optimization for computationally intensive functions

A novel derivative-free algorithm, called optimization by moving ridge functions (OMoRF), for unconstrained and bound-constrained optimization is presented. This algorithm couples trust region methodologies with output-based dimension reduction to accelerate convergence of model-based optimization strategies. The dimension-reducing subspace is updated as the trust region moves through the function domain, allowing OMoRF to be applied to functions with no known global low-dimensional structure. Furthermore, its low computational requirement allows it to make rapid progress when optimizing high-dimensional functions. Its performance is examined on a set of test problems of moderate to high dimension and a high-dimensional design optimization problem. The results show that OMoRF compares favourably with other common derivative-free optimization methods, even for functions in which no underlying global low-dimensional structure is known.

Research on Optimization of Coal Slime Fluidized Bed Boiler Desulfurization Cooperative Operation

The semi-dry desulfurization of slime fluidized bed boilers (FBB) has been widely used due to its advantages of low cost and high desulfurization efficiency. In this paper, the cooperative optimization of a two-stage desulfurization processes in the slime fluidized bed boiler was studied, and a model-based optimization strategy was proposed to minimize the operational cost of the desulfurization system. Firstly, a mathematical model for the FBB with a two-stage desulfurization process was established. The influences of coal slime elements on combustion flue gas and the factors that may affect the thermal efficiency of the boiler were then analyzed. Then, on the basis of the developed model, a number of parameters affecting the SO2 concentration at the outlet of the slime fluidized bed boiler were simulated and deeply analyzed. In addition, the effects of the sulfur content of coal slime, excess air coefficient, and calcium to sulfur ratio were also discussed. Finally, according to the current SO2 emission standard, the optimization operation problems under different sulfur contents were studied with the goal of minimizing the total desulfurization cost. The results showed that under the same sulfur content, the optimized operation was able to significantly reduce the total desulfurization cost by 9%, consequently improving the thermal efficiency of the boiler, ensuring the stable and up-to-standard emission of flue gas SO2, and thus achieving sustainable development.

Model-based optimization strategies for chromatographic processes: a review

Many decisions must be made when determining design and operation of a chromatographic process. Such decisions should optimize the performance, including product purity, recovery, productivity, and desorbent consumption. For this problem, model-based optimization has been shown to be a powerful approach, aided by recent developments in modeling of chromatographic processes. In this article, past studies on model-based optimization for single and multi-column chromatographic processes are reviewed. Focus is placed on applications of mathematical programming techniques. More challenging problems including online optimization and flowsheet design are also discussed.

Optimal Control of Droplets on a Solid Surface Using Distributed Contact Angles.

Controlling the shape and position of moving and pinned droplets on a solid surface is an important feature often found in microfluidic applications. However, automating them, e.g., for high-throughput applications, rarely involves model-based optimal control strategies. In this work, we demonstrate the optimal control of both the shape and position of a droplet sliding on an inclined surface. This basic test case is a fundamental building block in plenty of microfluidic designs. The static contact angle between the solid surface, the surrounding gas, and the liquid droplet serves as the control variable. By using several control patches, e.g., like that performed in electrowetting, the contact angles are allowed to vary in space and time. In computer experiments, we are able to calculate mathematically optimal contact angle distributions using gradient-based optimization. The dynamics of the droplet are described by the Cahn-Hilliard-Navier-Stokes equations. We anticipate our demonstration to be the starting point for more sophisticated optimal design and control concepts.

Learning Model Parameters for Decentralized Schedule-Driven Traffic Control

Model-based intersection optimization strategies that produce signal timings over a specified optimization horizon have been widely investigated for urban traffic signal control, and recent work in this area produced a scalable approach to real-time traffic control based on a decentralized schedule-driven optimization model. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection through local sensors and in real-time constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current look-ahead horizon. Intersections then exchange schedule information with their neighbors to achieve network level coordination. Although the approach is general and has demonstrated substantial success, its effectiveness in a given road network depends on the extent to which various parameters of the model, e.g, maximum green time, are adjusted to match that network's actual flow conditions over time. To address this problem, we propose a two-stage hierarchical structure that combines online planning and reinforcement learning. Reinforcement learning is applied to adjust the parameters of the model over a longer time-scale. On the other hand, online planning is used to compute the schedule for managing the traffic signals in the shorter term. We demonstrate how this hybrid approach outperforms the original approach in real-time traffic signal control problems.

Balancing-Aware Charging Strategy for Series-Connected Lithium-Ion Cells: A Nonlinear Model Predictive Control Approach

Charge unbalancing in series-connected cells can lead to lower storage capacity and shorter battery life. Model-based optimization strategies have proven to be very effective in addressing this problem. In this article, we propose a general nonlinear model predictive control (NMPC) scheme for obtaining a balancing-aware optimal charging. The presented method relies on an electrochemical model, tailored for control purposes. In view of the possibility of practical implementation, the concepts are subsequently specialized for an easily implementable power supply scheme. Finally, the NMPC approach is validated on commercial cells using a detailed battery simulator, with sound evidence of its effectiveness both under the assumption of full state availability and in the presence of an observer scheme.

Traffic density estimation using progressive neural architecture search

The current research work is to develop an automated system for estimating the traffic density of roads at traffic junctions and proposed a traffic control algorithm for smooth movement of vehicles based upon the information provided by the density estimation algorithm. The existing methodologies of hand-crafted features and rule-based estimation to count the number of vehicles has been replaced here by Convolutional Neural Network (CNN). The conventional CNN has been replaced by Progressive Neural Architecture (PNAS) which is based on sequential model-based optimization (SMBO) strategy.