Optimization Procedure Research Articles

Performance Analysis and Optimization of Supercritical CO2 Recompression Brayton Cycle Coupled With Organic Flash Cycle With a Two-Phase Expander

Abstract In this work, a combined supercritical CO2 recompression Brayton cycle (SCRBC)/organic flash cycle with a two-phase expander (OFCT) system is proposed to improve the thermal efficiency of the SCRBC, which utilizes a two-phase expander to replace the high-pressure throttling valve of a basic organic flash cycle (OFC). In addition, the OFCT is coupled at the waste heat end of the SCRBC as the bottom cycle for the use of waste heat at low temperatures. A comprehensive comparison is carried out for different organic working fluids, including the R123, R245fa, R142B, R236ea, and R600, regarding the thermal performance, environmental effect, and safety levels. Furthermore, influences of various factors on the thermal performance of the combined SCRBC/OFCT cycle are also examined, including the top cycle pressure ratio, top cycle turbine inlet temperature, mass flowrate ratio, evaporation temperature, and the condenser's pinch point temperature difference. A multi-objective optimization approach is employed on the combined SCRBC/OFCT system, which considers both the thermal efficiency and the specific investment cost as the objective function, and the optimization procedure is implemented through the nondominated sorting genetic algorithm II (NSGA-II) algorithm. The Pareto solution set and the compromise solution are finally obtained. The results indicate that the optimized combined SCRBC/OFCT system can improve the thermal efficiency by 11.75% and 9.70% when compared with the SCRBC and SCRBC/OFC, respectively.

Measuring and Mitigating the Risk of Advanced Cyberattackers

Sophisticated cyberattackers (commonly known as advanced persistent threats (APTs)) pose enormous risks to organizations such as financial institutions, industrial and commercial firms, government institutions, and power grids. This study presents a method and an index to measure the vulnerability of organizations to APT risk and shows why a one-size-fits-all solution to mitigate APT risk does not exist. Our vulnerability index is based on a model that describes the optimal behavior of a cyberattacker (APT) with research and development capabilities aspiring to attack a network that manages the organization and a network operator that deploys blocking and detection measures to protect its organization from the attack. We demonstrate how our vulnerability index, which accounts for the network’s structure and the APTs’ resources and strategy, can be used in realistic risk assessments and optimal resource allocation procedures and serve as a benchmark for organizations’ preparedness against APTs’ cyberattacks. We also propose that regulatory agencies of financial (and other) institutions provide the parameters that define an APT’s profile and request, as part of their periodic assessments of the organizations that they regulate, that our (or similar) vulnerability index will be reported to them by the regulated institutions. Finally, the viability of our index in modeling modern cybersecurity defense procedures shows that not only there is no silver bullet defense against all types of APTs, it is also imperative to account for APTs’ heterogeneity because detection and blocking measures can be complements, substitutes, or even degrade each other. For example, when the attacker’s (defender’s) budget is extremely large (small), the defender should deploy only detection measures, strongly advocating Zero Trust practices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0072 .

High-performing organic/quantum dot hybrid upconversion device based on a single-component near-infrared-sensitive layer

A donor/acceptor (D/A) heterojunction with an interfacial energetic offset is demonstrated to enable efficient exciton dissociation in organic photodetectors and upconversion devices (UCDs). Unfortunately, this approach usually encounters complicated optimization procedures and interfacial instability. Herein, we present an alternative strategy for achieving high-performing UCDs by utilizing an organic single-component near-infrared (NIR)-sensitive layer instead of a D/A heterojunction. The showcased UCD is constructed by vertically stacking an organic single-component Y6 NIR-detection unit and a quantum dot light-emitting unit. Due to the high dielectric constant and low exciton binding energy of the non-fullerene acceptor Y6, free carriers are directly and spontaneously generated upon NIR light excitation. As a result, the single-component UCD achieves a low light detection capability of 2.5 μW/cm2, a fast refresh rate of >3.8 × 104, and a high resolution exceeding 1100 dpi, providing a stable optical response to high-frequency NIR signals and high-quality NIR imaging.

BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference.

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to simultaneously sample from the space of causal configurations. We use these samples to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and different noise paradigms, as defined by the relative genetic contributions of causal and non-causal variants. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer's disease. In comparison to the baselines, only BEATRICE can successfully find the APOE allele, a commonly associated variant of Alzheimer's. Thus, we show that BEATRICE is a valuable tool to identify causal variants from eQTL and GWAS summary statistics across complex diseases and traits.

Decentralized distributionally robust chance-constrained operation of integrated electricity and hydrogen transportation networks

Hydrogen energy offers a promising pathway to decarbonizing future energy systems. Renewable energy-based hydrogen production and road transportation-based hydrogen delivery have strengthened the coupling between the power distribution network (PDN) and the hydrogen transportation network (HTN). In this article, we propose a decentralized distributionally robust chance-constrained (DRCC) method for the integrated electricity and hydrogen transportation network (IEHTN) to foster synergies between PDN and HTN. First, an optimal scheduling framework is developed to coordinate hydrogen production and transportation in the IEHTN, where multiple operational states of water electrolyzers and the time-constrained tube trailer routing are modeled. Second, a data-driven DRCC approach is proposed to model the uncertain behaviors of renewable power generation and hydrogen demand. A conditional value-at-risk approximation is then employed to convert the DRCC problem to a tractable mixed-integer linear program that can be solved by the off-the-shelf solver. Third, an alternating direction method of multipliers (ADMM) based solving scheme is designed to solve the problem in a distributed manner with limited information exchange between PDN and HTN. To address the non-convexity brought by binary variables in the ADMM, a tractable alternating optimization procedure (AOP) strategy is utilized to guarantee the convergence of solution. Finally, case studies are conducted on an IEHTN composed of the modified IEEE 33-bus PDN and 52-node HTN to verify the validity of the proposed method.

Design and Modeling of a Long Range Motion Compliant Nanopositioning Stage Driven by a Normal Stressed Electromagnetic Actuator

Compliant nanopositioning stages with built-in ultra-precision actuators are frequently integrated into production and analysis instruments comprising ultra-high precision motion generation systems. These stages are essential nanotechnology and advanced material analysis components, providing precise positioning capabilities for various applications. However, in the practical engineering field, there is a lack of compliant nanopositioning stages that can achieve a long-range motion while maintaining accuracy, reliability, and compact size, which is the inspiration for this research. This paper investigates the design, modeling, and experimental testing of a long-range motion-compliant nanopositioning stage driven by a normal stressed electromagnetic actuator (NSEA). The nanopositioning stage components’ structural framework and working principle, including NSEA, bridge type distributed compliant (BTDC) mechanism, and the guiding mechanism, are fully examined to derive an analytical model. The analytical model is utilized in the sections that follow. Factors affecting the stroke and natural frequency of the nanopositioning stage are also illustrated. The optimization process of the nanopositioning stage is conducted in pursuit of a high-precision stage by specifically looking into the electromagnetic, BTDC mechanism, and guiding mechanism parameters. This optimization procedure also takes into account various design constraints, including stiffness, saturation flux density, and stress. Furthermore, the finite element analysis is used to verify the analytical model, and the results are discussed. The prototype is fabricated with reference to the analytical and finite element analysis results, and the experimental tests are conducted, including motion and natural frequency tests. In addition, a control system, which adopts both a proportional-integral-derivative controller and a damping controller, is designed to create a closed-loop system. Finally, the tracking performance of the stage was investigated, and a very minimal tracking error was observed. Overall, the comprehensive models and experimental tests proved the stage to be a good model which achieved the objective of the research.

Stress and temperature dependence of irradiation creep in zircaloy-4 studied using proton irradiation

This paper describes a series of experiments conducted to investigate the in-situ irradiation creep behavior of Zircaloy-4 using proton irradiations as a surrogate to neutrons. The first series of experiments investigated the impact of the initial irradiation-induced defect evolution during the 0 – 0.1 dpa regime on the subsequent in-situ steady-state behavior derived from experimentation. These experiments were conducted at a constant applied load of 85 MPa, a constant damage rate of 1.58 × 10−6 dpa/s, and were repeated at both 250 °C and 350 °C. We also examined the use of a ‘conditioning-irradiation’ step prior to the creep tests on the results derived from subsequent in-situ proton irradiation creep experiments. By extension, we aimed to further develop and refine optimum testing procedures when using proton irradiations to investigate the in-situ creep behavior of nuclear materials. The second series of in-situ proton irradiation experiments were conducted on two Recrystallized (RX) Zircaloy-4 samples in order to investigate the temperature and stress dependence of irradiation creep. One sample was held at a constant load while the temperature was varied in the range of 275 – 350 °C, and the other was held at a constant temperature while stress was varied in the range of 65 – 105 MPa. The associated strains and creep rates were measured at each interval and used to determine an activation temperature of 5000 ± 1700 K and a stress sensitivity exponent of 4.3 ± 0.8 for RX Zr-4 over the given temperature and stress ranges. A discussion of potential deformation mechanisms based on competition between bulk diffusion through the lattice and point defect diffusion enabling dislocation climb and glide is given: the relatively high stress dependence suggested the latter is more likely however further investigations will be required to improve the mechanistic understanding. The results presented in this manuscript, including activation temperature, stress sensitivity, and associated creep rates, determined through proton irradiation investigations are closely comparable to those determined from neutron irradiation experiments found in the literature for similar zirconium alloys at similar temperatures and stresses. Coupled with the primary-secondary creep behavior investigations presented, this demonstrates the usefulness of this approach to estimate neutron-irradiation equivalent creep behavior using in-situ proton irradiation.

Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics.

Capillary electrophoresis coupled with tandem mass spectrometry (CE-MS/MS) offers advantages in peak capacity and sensitivity for metabolic profiling owing to the electroosmotic flow-based separation. However, the utilization of data-independent MS/MS acquisition (DIA) is restricted due to the absence of an optimal procedure for analytical chemistry and its related informatics framework. We assessed the mass spectral quality using two DIA techniques, namely, all-ion fragmentation (AIF) and variable DIA (vDIA), to isolate 60-800 Da precursor ions with respect to annotation rates. Our findings indicate that vDIA, coupled with the updated MS-DIAL chromatogram deconvolution algorithm, yields higher spectral matching scores and annotation rates compared to AIF. Additionally, we evaluated a linear migration time (MT) correction method using internal standards to accurately align chromatographic peaks in a data set. Postcorrection, the data set exhibited less than 0.1 min MT drifts, a difference mostly equivalent to that of conventional reverse-phase liquid chromatography techniques. Moreover, we conducted MT prediction for metabolites recorded in mass spectral libraries and metabolite structure databases containing a total of 469,870 compounds, achieving an accuracy of less than 1.5 min root mean squares. Our platform provides a peak annotation platform utilizing MT information, accurate precursor m/z, and the MS/MS spectrum recommended by the metabolomics standards initiative. Applying this procedure, we investigated metabolic alterations in lipopolysaccharide (LPS)-induced macrophages, characterizing 170 metabolites. Furthermore, we assigned metabolite information to unannotated peaks using an in silico structure elucidation tool, MS-FINDER. The results were integrated into the nodes in the molecular spectrum network based on the MS/MS similarity score. Consequently, we identified significantly altered metabolites in the LPS-administration group, where glycinamide ribonucleotide, not present in any spectral libraries, was newly characterized. Additionally, we retrieved metabolites of false-negative hits during the initial spectral annotation procedure. Overall, our study underscores the potential of CE-MS/MS with DIA and computational mass spectrometry techniques for metabolic profiling.

Synthesis of functional derivatives of chlorine-substituted isothiazoles.

Optimal procedures have been developed for the synthesis of substituted isothiazoles containing an active chlorine atom in position 5 of the heterocycle and various functional groups in position 3: carboxyl, hydroxymethyl, aldehyde. Based on these methods, previously undescribed compounds were obtained: 5-morpholino-substituted 3-hydroxymethyl-4- chloroisothiazole, 4-chloroisothiazole-3-carboxylic acid and its methyl ester. The resulting substances are reactive building blocks for organic synthesis of compounds with a high potential for biological activity.

Priority rules for handling containers to improve energy consumption and terminal efficiency

AbstractThis paper addresses the optimization of the yard crane handling processes in a container terminal to reduce energy consumption and improve overall system performance. More precisely, the paper presents and evaluates different sequencing rules, based on predefined priorities, to organize the rail yard to minimize moves during the rail loading operations. The minimization of overall energy consumption and maximum tardiness are considered, simultaneously assessing these two components of the objective function to better understand how they interact and how they can be optimized together. As a novel issue in optimization, a hill climbing algorithm is implemented, searching for the yard configuration that most improves the efficiency of container handling while being able to integrate different management rules of the terminal. The reference case study is the PSA Pra terminal in Genoa, Italy. A full rail yard with known delivery times, and crane operating along a single stack, is the operative scenario. Random due time sequences are generated during test instances, while technical data of crane are used. Moreover, crane movements involve both loading and unloading along multiple axes. From the results, the best priority rules improve energy consumption and lateness of the initial configuration of the yard by up to 55%, thus allowing the terminal management to reorganize the storage areas accordingly and improve their efficiency. The proposed priority rules bridge the gap between theoretical optimization procedures and container terminal practices.

Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine

Hydrogen fueled gas turbine is an efficient and environmentally friendly energy conversion equipment. However, it is prone to gas leakages from corrosion-prone components and pipe connections. In order to address gas leakage problems, source term estimation (STE) can be applied to estimate the location and intensity of the leakage sources. Traditional STE methods are based on an optimization procedure using an atmospheric transport and dispersion model, which requires considerable computation efforts and cannot meet the need of real-time implementation. The complex flow field around the gas turbine, the possible existence of multiple leakage sources, and the high-dimensional time series data further increase the difficulty of the STE problem. To address these challenges, in the present study, a deep learning based STE approach is proposed. The basic idea is to use long short-term memory auto-encoder (LSTM-AE) network to extract the encoded features of multi-sensor data and then use a deep neural network (NN) to establish the relationship between the encoded features and the source term parameters of hydrogen leakages. The multi-sensor data are generated using computational fluid dynamics (CFD) analysis to simulate relevant hydrogen leakage scenarios of concern. The results show that compared with other machine learning algorithms, the proposed approach has an overall best performance in terms of the STE accuracy. Specifically, its hydrogen leakage source localization accuracy is 0.9798, and the leakage strength estimation R-squared (R2) can reach 0.9632. When the training data proportion is only 20–30%, the proposed approach can still perform well, indicating the ability of the model to effectively predict the location and strength of the leakage source even with relatively less training data.

Revolutionizing WBAN communication for public health: Effective Optimization strategy-driven health assessment

This study explores the optimization procedures that are critical to enhancing WBAN communication efficacy in an environment of public health initiatives. In order to observe someone's physiological characteristics continuously and non-invasively, wireless body area networks (WBANs), are an essential piece of innovation for distant health observation. The WBAN individual nodes, yet, might relocate and lose reception from a distant base station over time. Thus, an effective inter-WBAN interaction approach is required to address this issue. In order to lower cost associated with networks and maximize cluster lifespan towards the network's effectiveness, this research develops a clustering-driven routing approach that uses cluster head serving as a gateway. For efficient cluster head selection, a novel customized chicken swarm optimization (CCSO) technique was introduced. Various parameter adjustments are made in multiple trials to verify the suggested approach effectiveness. The research conduct comprehensive comparison analysis of suggested and existing algorithms, several tests are conducted. The results show that the approach technique can increase the effectiveness of WBAN engagement and create clusters more quickly than the other methods in the framework of public health initiatives.

Optimization of Bacillus licheniformis’ nutrient system and oilfield trial for MEOR

Abstract The efficacy of microbial-enhanced oil recovery (MEOR) is contingent upon the performance of oil recovery functional bacteria. The optimization of the nutrient system for oil recovery functional bacteria can enhance their performance. In this study, the nutrient systems of the strains underwent screening and optimization procedures, utilizing single-factor combined with response surface methodology. Subsequently, field trials were conducted to assess the efficacy of MEOR. The results indicated that the carbon source in the nutrient system was sucrose, the nitrogen source was NaNO3, and the phosphorus source was a combination of KH2PO4 and Na2HPO4 (in a 1:1 ratio). The combination of the results from the single-factor experiments and the response surface experiment led to the following formulation for the strain F-T nutrient system (g/L): sucrose 10.0, NaNO3 2.0, KH2PO4 1.5, Na2HPO4 1.5, CaCl2 0.12, FeSO4 0.12, MgSO4·7H2O 0.2, Na2MoO4 0.08, yeast powder 1.0, and pH 7.2-7.5. The optimized F-T bacterium has the potential to spread oil up to a diameter of 40.50 mm. The results of the MEOR field trial indicated a 2.95 × 10 t increase in oil production, with a success rate of 83.87%. Following the implementation of MEOR, production levels were observed to decline to levels comparable to those observed prior to the intervention. This indicates that continued MEOR field applications may be necessary to further enhance crude oil recovery.

P073 KNOWLEDGE OF HYPERTENSION AND HOME BLOOD PRESSURE MONITORING AMONG HYPERTENSIVE PATIENTS IN INDIA: RESULTS FROM THE GRAND STUDY

Background and Objective: Hypertension remains a significant risk factor for cardiovascular diseases in India, contributing substantially to cardiovascular mortality. Despite the rising prevalence of hypertension, blood pressure control among hypertensive patients remains low, emphasizing the need for improved hypertension management and treatment. Home blood pressure monitoring (HBPM) serves as a vital tool in hypertension management, yet its recognition and utilization in developing countries like India remain limited. This study aimed to assess the knowledge of hypertension and HBPM among hypertensive patients across 18 medical centers in 12 States in India. Methods: A survey was conducted among 1,920 hypertensive patients, analyzing the patients’ background, the reasons for home blood pressure (HBP) measurement and non-HBP measurement, and associated factors to knowledge levels of hypertension and HBPM. Results: Findings revealed that 47.5% were measuring HBP, with reasons mainly attributed to physicians’ recommendations, while cost and lack of physician guidance were common reasons for non-HBP measurement. High knowledge levels of hypertension and HBPM significantly associated with measuring HBP (OR 16.23; 11.28-23.35, P-value <0.001), however, a significant gap existed in adherence to the optimal guidelines-based procedures of HBPM with only 40.7% following any 2 out of 8 steps to measure HBP. Conclusions: These results highlight the necessity for increased physicians’ recommendations of HBP measurement and providing clear instructions for measuring HBP aligned with guideline-based procedures.

The Importance of Structural Water in HDAC8 for Correct Binding Pose Applied for Drug Design of Anticancer Molecules.

Validating the docking procedure and maintaining the structural water molecules at HDAC8 catalytic site. Molecular docking simulations play a significant role in Computer-Aided Drug Design, contributing to the development of new molecules. To ensure the reliability of these simulations, a validation process called "self-docking or re-docking" is employed, focusing on the binding mode of a ligand co-crystallized with the protein of interest. In this study, several molecular docking studies were conducted using five X-ray structures of HDAC8-ligand complexes from the PDB. Ligands initially complexed with HDAC8 were removed and re-docked onto the free protein, revealing a poor reproduction of the expected binding mode. In response to this, we observed that most HDAC8-ligand complexes contained one to two water molecules in the catalytic site, which were crucial for maintaining the cocrystallized ligand. These water molecules enhance the binding mode of the co-crystallized ligand by stabilizing the proteinligand complex through hydrogen bond interactions between ligand and water molecules. Notably, these interactions are lost if water molecules are removed, as is often done in classical docking methodologies. Considering this, molecular docking simulations were repeated, both with and without one or two conserved water molecules near Zn+2 in the catalytic cavity. Simulations indicated that replicating the native binding pose of co-crystallized ligands on free HDAC8 without these water molecules was challenging, showing greater coordinate displacements (RMSD) compared to those including conserved water molecules from crystals. The study highlighted the importance of conserved water molecules within the active site, as their presence significantly influenced the successful reproduction of the ligands' native binding modes. The results suggest an optimal molecular docking procedure for validating methods suitable for filtering new HDAC8 inhibitors for future experimental assays.

Optimizing Skin Surface Metabolomics: A Comprehensive Evaluation of Sampling Methods, Extraction Solvents, and Analytical Techniques

Characterizing the metabolite fingerprint from the skin surface provides invaluable insights into skin biology and microbe-host interactions. To ensure data accuracy and reproducibility, it is essential to develop standard operating procedures for skin surface metabolomics. However, there is a notable lack of studies in this area. In this study, we thoroughly evaluated different sampling materials, extraction solvents, taping methods (frequency and number of tapes), and analytical techniques to optimize skin surface metabolomics. Our results showed that the combination of D-Squame D100 tape with a methyl tert-butyl ether/methanol extractant is optimal for skin surface lipidomics. Performing the skin-taping procedure 5 times with 1 tape yields sufficient biomass for lipid analysis, whereas the optimal taping procedure varies for water-soluble compounds. In addition, our study identified associations among the skin surface metabolites, some of which potentially underlie the formation of microbial cutotypes and offer insights into host-microbe interactions.

Automatic Aircraft Landing System: A Review

The aircraft landing phase, marking the finishing of a flight plane, is a critical yet dangerous aerial maneuver. Even though it might seem simple, predicting the complexities of performance during landing presents a big challenge due to the dynamic characteristics of the phase, interaction with piloting methods, as well as inherent uncertainties in aerodynamics. Landing is executed in close proximity to the ground at reduced airspeed, the landing phase entails an escalated safety risk. Notably, incidents and accidents, particularly overruns where aircraft fail to slow sufficiently on the runway before landing, underscore the imperative for advanced landing technologies. This paper presents a comprehensive review of research spanning from 2000 to the present exploring smart techniques like fuzzy logic and machine learning, an array of control strategies ranging from classic PID control to sophisticated hybrid control approaches, and optimization procedures utilizing diverse optimization algorithms. The primary objective is to provide a comprehensive evaluation of the existing research landscape, offering insights that can propel the evolution of strategies for safer and more efficient landings and making sure the plane touches down safely.

Determinant factor of environmental quality based on carbon footprint levels in Asia using regression model and Jacobian optimization method

Abstract The rapid economic growth in Asia has resulted in significant carbon emissions, leading to global climate change and environmental degradation. The situation is closely linked to increased industrial activities, population growth, and high energy consumption. Thus, this research is focused on analyzing whether the growth domestic product per capita, foreign direct investment per capita, energy use per capita, and forestland emissions per capita are associated with carbon footprint records in Asia. The attempt is conducted by employing parameter estimation of carbon footprint using a regression model and Jacobian Optimization procedure. The model incorporates data from FAOSTAT, Global Footprint Network, and the World Bank in 2021. The result suggested that if GDP per capita increases by 1,000 units, then the estimated average of carbon footprint per capita will also increase by 0.2, holding other variables remain constant. The finding contributes to formulating effective strategies for carbon footprint reduction and sustainable development in the region. The optimized parameter estimation provides valuable insights into the factors affecting the carbon footprint in Asia.

Development of a multi-fidelity optimisation strategy based on hybrid methods

A multi-fidelity optimisation strategy has been developed in the present work, and its performance is illustrated through a series of test cases. The strategy is based on hybrid methods such that two genetic optimisation algorithms are employed, each coupled to a different fidelity level with transfer of information between them. The aim is that the low fidelity model, being less accurate but with a lower computational cost, performs a comprehensive search along the design space guiding the high fidelity model to the optimum region. This strategy has been shown to reduce the computational time of an optimisation through analytical test cases as well as numerical cases. The analytical cases have been used to tune the parameters that define the multi-fidelity strategy, while the numerical cases are employed to apply the method to engineering problems, focusing on the aerodynamic performance of an airfoil. The speed-up shows a certain dependency to the models relation, both regarding their similarity level as well as the relative computational cost. For cases exhibiting a significant dissimilarity between models, wherein the low fidelity model is notably inaccurate, the attained speed-up diminishes, and numerous instances demonstrate an absence of speed-up. However, for most cases, even with poor model similarity the optimisations are accelerated by an order of 2, while values up to 3–5 were found for higher similarity levels. Hence, the developed strategy shows a relevant decrease of computational cost of an optimisation procedure although its performance is affected by the models relative accuracy.

Globalization of distributed parameter self‐optimizing control

AbstractNumerous nonlinear distributed parameter systems (DPSs) operate within an extensive range due to process uncertainties. Their spatial distribution characteristic, combined with nonlinearity and uncertainty, poses challenges in optimal operation under two‐step real‐time optimization (RTO) and economic model predictive control (EMPC). Both methods necessitate substantial computational power for prompt online reoptimization. Recent local distributed parameter self‐optimizing control (DPSOC) achieves optimality without repetitive optimization. However, its effectiveness is confined to a narrow range around a nominal operation. Here, globalized DPSOC is introduced to surmount the limitation of the local DPSOC. A global loss functional concerning controlled variables (CVs) is formulated using linear operators and Fubini's theorem. Minimizing the loss with a numerical optimization procedure yields CVs exhibiting global optimality. Maintaining these CVs at constants ensures such a process has a minimal average loss in a large operating space. The effectiveness of the proposed method is substantiated through a transport reaction simulation.