Operating Conditions Research Articles

Optimization of Adaptive Sliding Mode Controllers Using Customized Metaheuristics in DC-DC Buck-Boost Converters

Metaheuristics have become popular tools for solving complex optimization problems; however, the overwhelming number of tools and the fact that many are based on metaphors rather than mathematical foundations make it difficult to choose and apply them to real engineering problems. This paper addresses this challenge by automatically designing optimization algorithms using hyper-heuristics as a master tool. Hyper-heuristics produce customized metaheuristics by combining simple heuristics, guiding a population of initially random individuals to a solution that satisfies the design criteria. As a case study, the obtained metaheuristic tunes an Adaptive Sliding Mode Controller to improve the dynamic response of a DC-DC Buck–Boost converter under various operating conditions (such as overshoot and settling time), including nonlinear disturbances. Specifically, our hyper-heuristic obtained a tailored metaheuristic composed of Genetic Crossover- and Swarm Dynamics-type operators. The goal is to build the metaheuristic solver that best fits the problem and thus find the control parameters that satisfy a predefined performance. The numerical results reveal the reliability and potential of the proposed methodology in finding suitable solutions for power converter control design. The system overshoot was reduced from 87.78% to 0.98%, and the settling time was reduced from 31.90 ms to 0.4508 ms. Furthermore, statistical analyses support our conclusions by comparing the custom metaheuristic with recognized methods such as MadDE, L-SHADE, and emerging metaheuristics. The results highlight the generated optimizer’s competitiveness, evidencing the potential of Automated Algorithm Design to develop high-performance solutions without manual intervention.

Closing the Loop in the Carbon Cycle: Enzymatic Reactions Housed in Metal-Organic Frameworks for CO2 Conversion to Methanol.

The preparation of value-added chemicals from carbon dioxide (CO2) can act as a way of reducing the greenhouse gas from the atmosphere. Industrially significant C1 chemicals like methanol (CH3OH), formic acid (HCOOH), and formaldehyde (HCHO) can be formed from CO2. One sustainable way of achieving this is by connecting the reactions catalyzed by the enzymes formate dehydrogenase (FDH), formaldehyde dehydrogenase (FALDH), and alcohol dehydrogenase (ADH) into a single cascade reaction where CO2 is hydrogenated to CH3OH. For this to be adaptable for industrial use, the enzymes should be immobilized in materials that are extraordinarily protective of the enzymes, inexpensive, stable, and of ultra-large surface area. Metal-organic frameworks (MOFs) meet these criteria and are expected to usher in the much-awaited dispensation of industrial biocatalysis. Unfortunately, little is known about the molecular behaviour of MOF-immobilized FDH, FALDH, and ADH. It is also yet not known which MOFs are most promising for industrial enzyme-immobilization since the field of reticular chemistry is growing exponentially with millions of hypothetical and synthesized MOF structures reported at present. This review initially discusses the properties of the key enzymes required for CO2 hydrogenation to methanol including available cofactor regeneration strategies. Later, the characterization techniques of enzyme-MOF composites and the successes or lack thereof of enzyme-MOF-mediated CO2 conversion to CH3OH and intermediate products are discussed. We also discuss reported multi-enzyme-MOF systems for CO2 conversion cognizant of the fact that at present, these systems are the only chance of housing cascade-type biochemical reactions where strict substrate channelling and operational conditions are required. Finally, we delve into future perspectives.

H Induced Metal-Insulation Transition Boosts the Stability of High Temperature Polymer Electrolyte Membrane Fuel Cells.

High temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) have garnered significant attention due to their expanded range of hydrogen sources and simplified management systems. However, the frequent start-up and shut-down (SU/SD)caused fuel starvationoperation condition seriously deteriorates the performance and lifetime of the fuel cell.In this manuscript, VO2was incorporated in the anodeto restrain fuel starvationof the electrode. Under the operation condition, the insulative VO2would reversiblytransform into metallic HxVO2byintercalating hydrogen, and HxVO2can automatically release hydrogen,whichcouldact as the hydrogen bufferand suppress reverse-current degradation. After the hydrogen release, the generated insulating VO2would prevent theside reactions during fuel starvation.Comparedto the traditionalPt anode, the electrodewith VO2showed much higheroutput powerand greatly improved durability after fuel starvation. This work demonstrates the in-situreversible hydrogen storage/release-controlled metal-insulator phase transition strategy to enhance the durability of fuel cells.

Automatic Tracking Control Based on Bayesian Optimization

Abstract. This paper discusses the control problem of intelligent tracking vehicle in complex dynamic environment, and puts forward a solution combining Bayesian optimization algorithm and PID control. Traditional PID control faces many challenges when dealing with time-varying and nonlinear systems, but the operating environment of intelligent tracking vehicles is complex and changeable, and it is difficult to adjust parameters in real time to adapt to different operating conditions. Therefore, this paper introduces Bayesian optimization algorithm to predict the optimal parameter combination of PID controller by constructing hyperparameter optimization model, thus effectively improving the control performance and robustness of the system.

Seasonal Effects on Outdoor Stability of Perovskite Solar Cells

AbstractThe critical challenge for the commercialization of perovskite solar cells (PSCs) is their operational stability. PSCs’ outdoor operation exposes the cells to a combination of stress factors that are difficult to reproduce by indoor testing due to diurnal and seasonal variations. This highlights the need for outdoor testing under operational conditions. The effect of climate conditions on outdoor operational lifetime/ degradation of n‐i‐p PSCs is systematically studied herein. Their lifetime indicators are determined in different seasons, and correlated with the outdoor irradiance and temperatures measured simultaneously. Based on this outdoor measurement analysis and indoor light cycling stability tests, it is suggested that ambient temperatures induce a more significant effect than the irradiance on the PSC's lifetime/ degradation. The study also suggests different roles played by the temperatures during the diurnal light versus dark periods: the day/ light time maximum temperatures have a more significant effect on the long‐term degradation. In contrast, minimum temperatures during the night/ dark cycles significantly affected the diurnal reversible degradation and the initial fast degradation. The results show that the commonly used lifetime indicators T80 and T50 are climate‐dependent, and their use for comparative purposes is valid only if measured in similar climatic conditions.

H Induced Metal‐Insulation Transition Boosts the Stability of High Temperature Polymer Electrolyte Membrane Fuel Cells

High temperature polymer electrolyte membrane fuel cells (HT‐PEMFCs) have garnered significant attention due to their expanded range of hydrogen sources and simplified management systems. However, the frequent start‐up and shut‐down (SU/SD) caused fuel starvation operation condition seriously deteriorates the performance and lifetime of the fuel cell. In this manuscript, VO2 was incorporated in the anode to restrain fuel starvation of the electrode. Under the operation condition, the insulative VO2 would reversibly transform into metallic HxVO2 by intercalating hydrogen, and HxVO2 can automatically release hydrogen, which could act as the hydrogen buffer and suppress reverse‐current degradation. After the hydrogen release, the generated insulating VO2 would prevent the side reactions during fuel starvation. Compared to the traditional Pt anode, the electrode with VO2 showed much higher output power and greatly improved durability after fuel starvation. This work demonstrates the in‐situ reversible hydrogen storage/release‐controlled metal‐insulator phase transition strategy to enhance the durability of fuel cells.

Shedding Light on the Active Species in a Cobalt-Based Covalent Organic Framework for the Electrochemical Oxygen Evolution Reaction.

While considerable efforts have been devoted to developing functionalized covalent organic frameworks (COFs) as oxygen evolution electrocatalysts in recent years, studies related to the investigation of the true catalytically active species for the oxygen evolution reaction (OER) remain lacking in the field. In this work, the active species of a cobalt-functionalized COF (TpBpy-Co) is studied as electrochemical OER catalyst through a series of electrochemical measurements and post-electrolysis characterizations. These results suggest that cobalt oxide-based nanoparticles are formed in TpBpy-Co from Co(II) ions coordinated to the COF backbone when exposing TpBpy-Co to alkaline media, and these newly formed nanoparticles serve as the primary active species for oxygen evolution. The study thus emphasizes that caution is warranted when assessing the catalytic activity of COF electrocatalysts, as the pristine COF may act as the pre-catalyst, with the active species forming only under catalyst operating conditions. Specifically, strong coordination between COFs and metal centers under electrochemical operation conditions is crucial to avoid unintended transformation of COF electrocatalysts. This work thus contributes to the rational development of earth-abundant COF OER catalysts for the production of green hydrogen from renewable resources.

A three-axis regime diagram for quantitative analyses of the mixing field structure in laminar and turbulent combustion

The combustion characteristics and stability are affected primarily by the mixing field structure. The goal of many practical systems is to achieve higher stability by creating Inhomogeneous, Partially Premixed, and Stratified (IPPS) environments. A three-axis regime diagram is proposed in this work to describe the mixing field structure of the IPPS, the non-premixed, and the premixed environments. The proposed axes of the diagram are the mean, the fluctuations, and the 2-D gradients of the conserved scalar mixture fraction.Highly resolved two-dimensional measurements of the mixture fraction in highly stabilized burners with well-controlled mixture inhomogeneity using advanced Rayleigh scattering measurements are used in this study to investigate the abilities of the proposed diagram. The effects of the mixing level, equivalence ratio, and Reynolds number on the mixing field structure were investigated using this diagram adequately. The three-axis diagram provided quantitative detailed information on the mixing field structure at different operating conditions. In addition, the local mixing layer thickness data are extracted from the diagram based on the mixture fraction profiles and the corresponding mixture fraction gradients profiles in the mixture fraction space.The level of mixture inhomogeneity and equivalence ratio significantly affect the mixing field structure, while the effect of the Reynolds number in turbulent conditions is weak. The correlations between the local mixing layer thickness and the main operating parameters are observed using its PDFs. Reducing the level of mixture inhomogeneity reduces the maximum mixture fraction gradients at zero fluctuations of the mean mixture fraction. The correlations are clear for further analytical investigation. This study shows that the proposed three-axis diagram is a useful tool to investigate and analyze the mixing field structure of the IPPS regimes.

Full-Scale Testing of Corrosion Resistant Alloy-Mechanically Lined Pipes for Submarine Pipelines

Abstract This study evaluates the fatigue performance of Corrosion Resistant Alloy–Mechanically Lined Pipes (CRA-MLPs), focusing on the critical triple point, where the CRA liner, weld overlay, and backing steel pipe intersect. Through Full-Scale Bending Tests (FSBT) and Full-Scale Resonance Fatigue Tests (FSFT), real-world operational and installation conditions were simulated to assess fatigue resistance, particularly at the triple point–an area not adequately addressed in current industry standards. The results reveal that the triple point exhibits excellent fatigue performance, exceeding the criteria of existing guidelines, despite the lack of specific provisions for this critical region. This study provides valuable data to inform updates to industry standards, enhancing the safety and reliability of subsea pipeline operations.

Revealing how internal sensors in a smart battery impact the local graphite lithiation mechanism

Smart batteries, i.e., equipped with internal and external sensors, are emerging as promising solutions to enhance battery state of health and optimize operating conditions. However, for accurate correlations between the evolution of the cell parameters (e.g., temperature, strain) and physicochemical degradation mechanisms, it is crucial to know the reliability of sensors. To address this question, we perform a synchrotron operando X-ray diffraction experiment to investigate the local and global impact of the presence of internal sensors on a commercial prismatic Li-ion battery cell at various (dis)charge rates. We find that, while the overall electrochemical performance is unaffected, the sensors have a substantial impact on the local graphite lithiation kinetics, especially at high (dis)charge rates. These results show the importance of controlling local deformations induced by internal sensors and tailoring the dimensions of these sensors to obtain reliable battery performance indicators and optimize smart batteries.

Enhanced dynamic modeling of regenerative CO2 transcritical power cycles: Comparative analysis of Pham-corrected and conventional turbine models

Turbine modeling plays a critical role in assessing system power output and performance in CO₂ transcritical power cycle (CTPC) systems, particularly under dynamic operating conditions. Conventional turbine models often fail to provide accurate predictions when inlet parameters deviate from the design point, leading to discrepancies in system behavior. This paper introduces a novel methodology by integrating a Pham-based corrected turbine performance map into a regenerative CTPC dynamic system. This approach enhances turbine modeling accuracy compared to traditional models, such as the uncorrected turbine performance map, nozzle model, and empirical model. The proposed Pham-corrected model method improves system response stability, particularly in terms of inlet pressure and CO₂ mass flow rate under heat source temperature reductions, offering more reliable performance predictions. In contrast, the empirical model shows substantial deviations in net power output (32 kW or 4.0 %) and thermal efficiency (1.36 %), leading to longer system stabilization times. The Pham-corrected and uncorrected turbine performance map models exhibit similar responses under cooling water mass flow rate reductions but diverge in turbine inlet pressure trends compared to the nozzle and empirical models. Additionally, the uncorrected turbine performance map model shows significant deviations in net power (25.6 kW or 3.17 %) and thermal efficiency (0.5 %) under a 10 % reduction in pump speed. This study highlights the importance of correcting turbine inlet parameters and addressing prediction deviations in conventional models. The Pham-corrected turbine model improves reliability, performance prediction, and design optimization, especially under fluctuating heat sources. With its potential to enhance system stability and efficiency, this methodology offers significant prospects for future CTPC applications, including waste heat recovery and renewable energy integration.

End of tubing placement optimization for multi-stage fractured horizontal wells by transient multiphase flow simulation

The exploitation of shale gas offers an effective pathway towards mitigating greenhouse gas emissions and promoting a clean and low-carbon energy system. Nonetheless, liquid loading has been commonly observed in the middle and late production stage, leading to sharp decline of shale gas production. How to improve unloading performance is challenging due to limited liquid-loading capacity. The strategic and timely installation of oil tubing serves as an effective means to mitigate wellbore fluid accumulation in the gas field. Thus, this work tries to improve gas recovery factor by optimizing the End of Tubing (EOT) location and the timing of tubing installation in shale gas wells through transient multiphase flow simulation. The transient simulation approach is adopted owing to its capability to accurately characterize the transient unloading process in gas wells and its closer approximation to field operation conditions compared to steady-state simulation methods. The casing production model and tubing production model have been developed to accurately simulate the flow behavior of fracturing fluid in the casing and tubing production, respectively. The annular flow and the coupling of fluid flow between reservoir and wellbore are the primary focuses of the two models aimed at analyzing fluid flow behavior and the total pressure drop (Δptotal). The casing production model is used to optimize the timing of tubing installation by simulating the liquid loading process of gas well including Δptotal and liquid inventory of wellbore. The optimal time of tubing placement is identified as the point when fluid starts to accumulate in the wellbore, as confirmed through the quantifiable analyses of Δptotal and liquid inventory. The tubing production model is further utilized to optimize the EOT location and tubing diameter considering variations in annular flow and the challenges associated with tubing installation. The placement of tubing in up-dip wells is recommended at Point A, which is determined by considering the influence of fluid gravity as a resistive force. Conversely, for down-dip wells, it is advisable to position the tubing between 1/3 of AB and 1/2 of AB as fluid gravity serves as a driving force in these scenarios. This work offers guidance for the optimum EOT location in shale gas reservoirs experiencing severe liquid loading. Consequently, it is essential to establish guidelines for determining the optimal EOT location across various horizontal well configurations and flow conditions.

Fluidized-bed gasification kinetics model development using genetic algorithm for biomass, coal, municipal plastic waste, and their blends

This study aims to solve the inverse problem of chemical kinetics with the genetic algorithm (GA) as an optimization tool to develop robust gasification kinetic models that target a variety of feedstocks, such as southern pine biomass, lignite coal, municipal plastic wastes, and their blends. A dataset (syngas compositions, tar compositions, and char yield) obtained by conducting oxy-steam fluidized-bed gasification experiments of various feedstocks, including southern pine biomass, lignite coal, municipal plastic waste, and their twelve different blends have been utilized to develop and validate gasification kinetic model. The results generated by the developed GA-based gasification model surpassed the performance of the conventional model (developed using a trial and error approach), with the least average absolute errors of 0.9 % for H2, 1 % for CO, 0.65 % for CO2, and 3 % for CH4 in the syngas prediction. The study contributed by developing individual gasification kinetics models for various feedstocks, including biomass, coal, plastics, and their blends. Furthermore, the model was found to be suitable for a range of gasification operating conditions, such as steam-to-carbon ratios of 1–3, equivalence ratios of 0–0.2, and gasifier temperatures of 750–950 °C.

Rolling Resistance Evaluation of Pavements Using Embedded Transducers on a Semi-Trailer Suspension

Road agency initiatives to reduce traffic-related greenhouse gas emissions are limited by the inability of current experimental methods to assess pavement impacts on vehicle energy consumption. This study addresses this by examining the rolling resistance of a semi-trailer suspension under highway conditions using a precise measurement system with embedded transducers. Data were collected over 174 km of highway, covering various pavement types under mild summer conditions. The analysis revealed notable differences in rolling resistance due to pavement characteristics, with more pronounced variations observed within pavement types than between them. For instance, geographically consecutive jointed rigid pavements showed a 34% variation in rolling resistance, likely correlated with harmonic excitations generated by slab presence, while flexible pavements exhibited up to a 21% variation under similar tire operating conditions. Composite pavements generally performed the worst, possibly due to interactions between bituminous materials and older cement-based foundations. The study also highlighted the critical role of tire operating conditions, showing a decrease of 0.09 kg/tonne in rolling resistance for every 1 °C increase in temperature. This research shows that precisely measuring the rolling resistance (±0.1 kg/tonne) in situ for heavy vehicles is feasible and underscores the need for additional data in diverse weather scenarios to better align laboratory results with on-road realities.

Optimal operation guidelines for direct recovery of high-purity precursor from spent lithium-ion batteries: hybrid operation model of population balance equation and data-driven classifier

The direct resynthesis of precursor from spent lithium-ion batteries (LIBs) via co-precipitation is a crucial step in closed-loop cathode recycling systems. However, design and operation strategies for producing high-purity precursors have not been comprehensively explored or optimized. Herein, we propose the optimization of co-precipitation during the recovery of spent LIBs to achieve impurity-free precursor resynthesis. By incorporating the thermodynamic equilibrium model of the leaching solution of spent LIBs into a population balance equation (PBE) model, we identified the operating ranges that prevented the formation of impurities. Bayesian optimization was employed within the screened operating ranges to determine the optimal operating conditions for minimizing both operation time and maximum particle size. This optimization was performed for both unseeded batch and semi-batch systems. The results demonstrate that the selection of an optimal semi-batch operation can reduce the operation time by 23.33% and increase the particle size by 54.75%, owing to the high nucleation and particle growth rate during the initial time step. By employing an optimization approach based on the PBE model, this study provides detailed operational guidelines for batch and semi-batch co-precipitation, enabling the production of high-purity precursor materials from spent LIBs, while minimizing both operating time and maximum particle size.

Calibrated Adaptive Teacher for Domain-Adaptive Intelligent Fault Diagnosis

Intelligent fault diagnosis (IFD) based on deep learning can achieve high accuracy from raw condition monitoring signals. However, models usually perform well on the training distribution only, and experience severe performance drops when applied to a different distribution. This is also observed in fault diagnosis, where assets are often operated in working conditions different from the ones in which the labeled data have been collected. The scenario where labeled data are available in a source domain and only unlabeled data are available in a target domain has been addressed recently by unsupervised domain adaptation (UDA) approaches for IFD. Recent methods have relied on self-training with confident pseudo-labels for the unlabeled target samples. However, the confidence-based selection of pseudo-labels is hindered by poorly calibrated uncertainty estimates in the target domain, primarily due to over-confident predictions, which limits the quality of pseudo-labels and leads to error accumulation. In this paper, we propose a novel method called Calibrated Adaptive Teacher (CAT), where we propose to calibrate the predictions of the teacher network on target samples throughout the self-training process, leveraging post hoc calibration techniques. We evaluate CAT on domain-adaptive IFD and perform extensive experiments on the Paderborn University (PU) benchmark for fault diagnosis of rolling bearings under varying operating conditions, using both time- and frequency-domain inputs. We compare four different calibration techniques within our framework, where temperature scaling is both the most effective and lightweight one. The resulting method—CAT+TempScaling—achieves state-of-the-art performance on most transfer tasks, with on average 7.5% higher accuracy and 4 times lower calibration error compared to domain-adversarial neural networks (DANNs) across the twelve PU transfer tasks.

Design Principle and Regulation Strategy of Noble Metal‐Based Materials for Practical Proton Exchange Membrane Water Electrolyzer

AbstractGreen hydrogen holds immense promise in combating climate change and building a sustainable future. Owing to its high power‐to‐gas conversion efficiency, compact structure, and fast response, the proton exchange membrane water electrolyzer (PEMWE) stands out as the most viable option for the widespread production of green hydrogen. However, the harsh operating conditions of PEMWE make it heavily dependent on noble metal‐based catalysts (NMCs) and incur high operational and maintenance costs, which hinder its extensive adoption. Hence, it is imperative to improve the performance and lifespan of NMCs and develop advanced components to reduce the overall costs of integrating PEMWE technology into practical applications. In light of this, the fundamental design principles of NMCs employed in acidic water electrolysis are summarized, as well as recent advancements in compositional and structural engineering to enhance intrinsic activity and active site density. Moreover, recent innovations in stack components of practical PEMWE and their impact on cost‐benefit and lifespan are presented. Finally, the current challenges are examined, and potential solutions for optimizing NMCs and PEMWE in electrocatalytic hydrogen production are discussed.

Insights on Prussian Blue Analogue Cathode Material Engineered with Polypyrrole Surface Protection Layer for Aqueous Rechargeable Zinc Metal Battery.

One of the key intricacies against using Prussian blue analogues (PBAs)in aqueous batteries is their gradual dissolution in aqueous electrolytes, resulting in inadequate cycling stability. Besides, the rate capability of PBAs is limited due to their poor electrical conductivity. To overcome these challenges, it is essential to tune the physical and chemical properties of PBAs at the nano regime without affecting the inherent charge storage properties, especially at high-voltage operating conditions. Through this work, a strategy is demonstrated to enhance the electrochemical performance of vanadium-based PBA (V-PBA) by surface engineering using a conducting polymer nano-skin (V-PBA/PPy) for aqueous zinc metal batteries. The polypyrrole (PPy) nano-skin over the V-PBA nanoparticles acts as an electron percolation path to ameliorate the poor electronic conductivity of the otherwise pristine V-PBA. Interestingly, the V-PBA with an optimized polypyrrole coating (V-PBA/PPy-2) exhibits an enhanced specific capacity (173 mAh g-1 at 0.10 A g-1) than the pristine V-PBA counterpart (80 mAh g-1) and 85% capacity retention up to 500 cycles. The DFT calculation confirms the synergistic interaction between PPy and V-PBA and the presence of PPy favors the adsorption of Zn.

Adoption of Multiphase and Variable Flux Motors in Automotive Applications

This paper investigates the incorporation of multiphase (MP) and variable flux (VF) permanent magnet motors to electric vehicles (EVs). A literature review is carried out first, which covers the characteristics, benefits and challenges of both motor configurations. MP motors, with their increased phase number, offer enhanced fault tolerance, reduced torque ripple, and improved efficiency, among other benefits. VF technology allows for dynamic adjustment of the magnetic field, optimising motor performance across various operating conditions. By integrating these technologies, this study aims to harness the benefits of both MP configurations and VF capabilities. For this reason, the Finite Element Method (FEM) is used with three-phase, MP, VF, and VF-MP motors. The main contribution is that both technologies have been implemented in a single motor to evaluate and quantify their impact together, obtaining higher torque and constant power values, lower torque ripples, and higher efficiencies in the whole working range.

Ibuprofen photodegradation promoted by ZnO and TiO2-P25 nanoparticles: A comprehensive kinetic, reaction mechanisms, and thermodynamic investigation

Photocatalytic activity, reaction kinetics, modeling, and thermodynamics of commercial TiO2-P25 and ZnO nanoparticles (NPs) for ibuprofen (IBU) photodegradation were investigated. Photodegradation experiments were performed in a batch reactor under UV irradiation. The photodegradation performances of TiO2-P25 and ZnO NPs were further studied and modeled under different operation conditions, by varying the reaction temperature, catalyst bulk density, and the initial concentration of the IBU solution. The descriptive kinetic models for the experimental data were tested, through the estimated kinetic parameters, together with the statistical information, revealing that the reaction rate in the case of TiO2-P25 is of first order while the ZnO NPs follow second-order kinetics with respect to IBU. The photodegradation mechanisms for both TiO2-P25 and ZnO NPs were determined to be Langmuir-Hinshelwood and Eley-Rideal, respectively. Thermodynamic parameters were assessed, particularly, changes in Gibbs free energy, enthalpy, and entropy indicating the efficient photodegradation performance of these NPs.