Optimal Operation Research Articles

A Study of the Rainwater Pumping Station Operation Plan to Reduce River Water Level

Urban flood damage is caused by various and complex effects such as inland flood and external water flooding, so research on this is necessary. This study presented a rainwater pumping station operation technique that can suppress the rise in water levels in discharge rivers based on rainfall and pump operation scenarios in order to reduce inland water flooding and simultaneously consider external water levels. The research method used genetic algorithm (GA) optimization techniques to calculate the optimal operating level for each pump, and based on this, an operating scenario was created to minimize pump operation. In addition, after constructing a rainfall scenario based on the probability rainfall in Seoul, an optimal pump operation technique that can reduce the external water level while minimizing the inundation of domestic water was proposed. The results of the study showed that as the rainfall intensity increases, the flooding increases and the peak depth of the runoff increases. In addition, as the pumping scenario changes from 1 to 11, the number of pumping operations decreases, which reduces the drainage volume of the pumps, which increases the flooding.

Simulation Optimization and Experimental Validation of Hydraulic Impact in Pruning Machines

Abstract This study addresses the instability caused by hydraulic shocks in fruit tree pruning machines during operation and proposes an optimization scheme that combines simulation with experimentation. We investigated the mechanisms of load shock in the hydraulic system through mathematical modeling and LS-DYNA simulation analysis. The optimal operating parameters were determined using mechanical and orthogonal analyses: a forward speed of 4 km/h, a pruning tool angle of 60°, and a saw blade rotation speed of 1200 r/min.Additionally, we employed AMESim simulation to optimize the hydraulic system of the pruning machine by designing a dual accumulator configuration to effectively suppress hydraulic shocks.We validated the optimized system through simulations and experimental tests, demonstrating significant reductions in pressure fluctuations, with cutting efficiency improved by 30% and pressure fluctuations reduced by 26.7%.This study not only presents an innovative dual accumulator design but also validates its effectiveness in pruning machines through simulations and experiments, thereby enhancing the operational efficiency and stability of agricultural machinery.

A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS2-Based Microparticles in a Breast Cancer Model.

Multimodal cancer therapies are often required for progressive cancers due to the high persistence and mortality of the disease and the negative systemic side effects of traditional therapeutic methods. Thus, the development of less invasive modalities for recurring treatment cycles is of clinical significance. Herein, a light-activatable microparticle system was developed for localized, pulsatile delivery of anticancer drugs with simultaneous thermal ablation by applying controlled ON-OFF thermal cycles using near-infrared laser irradiation. The system is composed of poly(caprolactone) microparticles of 200 μm size containing molybdenum disulfide (MoS2) nanosheets as the photothermal agent and hydrophilic doxorubicin or hydrophobic violacein, as model drugs. Upon irradiation, the nanosheets heat up to ≥50 °C leading to polymer softening and release of the drug. MoS2 nanosheets exhibit high photothermal conversion efficiency and require low-power laser irradiation. A machine learning algorithm was applied to acquire the optimal laser operation conditions. In a mouse subcutaneous model of 4T1 triple-negative breast cancer, 25 microparticles were intratumorally administered, and after 3-cycle laser treatment, the system conferred synergistic phototherapeutic and chemotherapeutic effects. Our on-demand, pulsatile synergistic treatment resulted in increased median survival up to 39 days post start of treatment compared to untreated mice, with complete eradication of the tumors at the primary site. Such a system is therapeutically relevant for patients in need of recurring cycles of treatment on small tumors, since it provides precise localization and low invasiveness and is not cross-resistant with other treatments.

Research on reaction mechanisms on chemical looping gasification of naphthalene over NiFe2O4 for syngas production: An experimental and theoretical study

The effective removal of tar during biomass gasification is a significant challenge that can be addressed by chemical looping gasification (CLG), which offers a promising alternative for converting biomass into syngas with reduced tar generation. In this study, we investigated the mechanism of CLG of naphthalene as a model biomass tar compound over NiFe2O4 to produce syngas. Both experimental and simulation results indicate the optimal operating conditions for syngas production are 1000℃ and an O/C ratio of 1:1. Under the optimal reaction conditions, the experimental and simulated CO yields were 67 vol% and 73 vol%, respectively, while H2 yields were 22 vol% and 24 vol%. Additionally, carbon conversion rates reached 78% and 76%. Thermodynamic calculations and molecular dynamics simulations revealed that the reactions proceed spontaneously, with increasing temperature favoring naphthalene conversion and syngas production. Density functional theory results indicate that the reaction pathway centered on the carbon at the α-position is the key to naphthalene and NiFe2O4 conversion. The optimal reaction pathways for H2 and CO formation were explored. This study on the CLG of naphthalene over NiFe2O4 provides a theoretical basis for the development of more efficient CLG processes with reduced tar formation.

Componentizing autonomous underwater vehicles by physical-running algorithms

Autonomous underwater vehicles (AUV) constitute a specific type of cyber-physical system that utilize electronic, mechanical, and software components. A component-based approach can address the development complexities of these systems through composable and reusable components and their integration, simplifying the development process and contributing to a more systematic, disciplined, and measurable engineering approach. In this article, we propose an architecture to design and describe the optimal performance of components for an AUV engineering process. The architecture involves a computing approach that carries out the automatic control of a testbed using genetic algorithms, where components undergo a ‘physical-running’ evaluation. The procedure, defined from a method engineering perspective, complements the proposed architecture by demonstrating its application. We conducted an experiment to determine the optimal operating modes of an AUV thruster with a flexible propeller using the proposed method. The results indicate that it is feasible to design and assess physical components directly using genetic algorithms in real-world settings, dispensing with the corresponding computational model and associated engineering stages for obtaining an optimized and tested operational scope. Furthermore, we have developed a cost-based model to illustrate that designing an AUV from a physical-running perspective encompasses extensive feasibility zones, where it proves to be more cost-effective than an approach based on simulation.

A Fuzzy Adaptive PID Coordination Control Strategy Based on Particle Swarm Optimization for Auxiliary Power Unit

Range extender hybrid vehicles have the advantages of better dynamics and longer driving range while reducing pollution and fuel consumption. This work focuses on the control strategy of an Auxiliary Power Unit (APU) operating in power generation mode for a range-extender mixer truck. When an operating point is switched, the engine speed and generator torque of the APU will switch accordingly. In order to ensure APU fast and stable adjustment to meet the power demand of the vehicle as well as operate at the lowest fuel consumption, a fuzzy adaptive PID coordination control strategy based on particle swarm optimization (PSO) is proposed to control the APU. The optimal operating curve of APU is calculated by coupling the engine and generator first. Then, the adaptive PID algorithm is used to control the speed and torque of the APU in a dual closed loop. The PSO is used to optimize the PID control parameter. Through hardware-in-the-loop tests under different working conditions, the control strategy is verified to be effective and real-time. The results show that the proposed control strategy can coordinate the operating of engine and generator and control the APU to track target power stably and quickly under minimum fuel consumption. Compared with traditional PID control strategy, the overshoot, regulation time and steady-state error are reduced by 55.1%, 11.1% and 77.3%, respectively.

Evaluating Convolutional Neural Network Architecture for Historical Topographic Hardcopy Maps Analysis: A Study on Training and Validation Accuracy Variation

Convolutional Neural Networks (CNN) are widely used for image analysis tasks, including object detection, segmentation, and recognition. Given the advanced capability, this study evaluates the effectiveness and performance of CNN architecture for analysing Historical Topographic Hardcopy Maps (HTHM) by assessing variations in training and validation accuracy. The lack of research specifically dedicated to CNN’s application in analysing topographic hardcopy maps presents an opportunity to explore and address the unique challenges associated with this domain. While existing studies have predominantly focused on satellite imagery, this study aims to uncover valuable insights, patterns, and characteristics inherent to HTHM through customised CNN approaches. This study utilises a standard CNN architecture and tests the model’s performance with different epoch settings (20, 40, and 60) using varying dataset sizes (288, 636, 1144, and 1716 images). The results indicate that the optimal operation point for training and validation accuracy is achieved at epoch 40. Beyond epoch 40, the widening gap between training and validation accuracy suggests overfitting. Hence, adding more epochs does not significantly improve accuracy beyond the optimum phase. The experiment also shows that the CNN model obtains a training accuracy of 98%, validation accuracy of 67%, and F1-score overall performance of 77%. The analysis demonstrates that the CNN model performs reasonably well in classifying instances from the HTHM dataset. These findings contribute to a better understanding of the strengths and limitations of the model, providing valuable insights for future research and refinement of classification approaches in the context of topographic hardcopy map analysis.

Artificial intelligence assisted prediction of optimum operating conditions of shell and tube heat exchangers: A grey‐box approach

AbstractIn this study, a Grey‐box (GB) model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger (STHE) under varying process conditions. Aspen Exchanger Design and Rating (Aspen‐EDR) was initially used to construct a first principle model (FP) of the STHE using industrial data. The Genetic Algorithm (GA) was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions. A dataset comprised of optimum process conditions was generated through FP‐GA integration and was utilised to develop an Artificial Neural Networks (ANN) model. Subsequently, the ANN model was merged with the FP model by substituting the GA, to form a GB model. The developed GB model, that is, ANN and FP integration, achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model. Performance of the GB framework was also comparable to the FP‐GA approach but it significantly reduced the computation time required for estimating the optimum process conditions. The proposed GB‐based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.

Operation Analysis of Power Systems with HVDC Interconnections Using a Transient Stability Aware OPF Method

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided.

Optimal Operation of the Power-to-Gas Storage System Considering Energy Optimization in the Independent Electrical System

Optimal Operation of the Power-to-Gas Storage System Considering Energy Optimization in the Independent Electrical System

Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards.

Analysis of the interfacial reaction between Si-based anodes and electrolytes in Li-ion batteries.

To ensure optimal operation of electrochemical energy storage devices, a precise design of the interfaces formed between the electrodes and the electrolyte is essential. Si is a promising anode active material for Li-ion batteries owing to its high theoretical capacity; however, the large volume change associated with Li absorption and release hinders its practical applications. The development of high-performance storage batteries depends on the construction of an electrode-electrolyte interface that facilitates Li+ movement. To this aim, the authors have developed Si-specific interface observation methods. In this feature article, we review and discuss recent advances in the reaction behavior of Si-based anodes, particularly at the Si electrode-electrolyte interface.

The efficient strategy of operating the active cooling systems incorporated with photovoltaic panels for the three climatic regions: A case study

ABSTRACT Photovoltaic stations in Algeria are considered an effective solution to overcome the problem of its lack of electrical power and achieve the goals of sustainable development. The weather in Algeria is continental, it has three main climate zones from north to south, and the climate in Algeria is desert, which causes an increase in the temperature of photovoltaic modules and thus a decrease in their production of electrical power. Then the active cooling systems that use water as a cooling medium represent a good option to reduce the PV module temperature and thus increase electrical power generation rates. Due to the change in weather in Algeria from north to south and during the four seasons, in some periods, the operation of cooling systems is ineffective and leads to a decrease in the net output electrical power of photovoltaic stations. Therefore, this manuscript aims to develop an advanced strategy for operating active cooling systems incorporated with PV stations to achieve the highest net electrical power output from PV stations in Algeria. To achieve this goal, the theoretical study was conducted on three different regions in Algeria that express its continental climate, namely, Algiers (36°46′ N, 3°03′ E), northern Algeria, and Djelfa (34°40′ N 3°15′ E) in central Algeria and Ghardaïa (32°29′ N 3°40′ E) in southern Algeria, to get the optimal cooling strategies that achieve the highest net electrical power produced by PV stations. The simulation results presented that the average improvement in the electrical power generated from the PV panels incorporated with active cooling systems reached 15.1%, 17.8%, and 19.7% under the climate conditions of Algiers, Djelfa, and Ghardaïa, respectively. The optimal operating strategies of active cooling systems integrated with PV stations in Algeria increase the average net output electrical power of the PV stations by a rate reached 7.65%, 7.13%, and 4.91% for Algiers, Djelfa, and Ghardaïa, respectively.

Reconfiguration of acid gas removal process matching the integration of coal chemical industry with green hydrogen

The focus of the acid gas removal process has shifted from decarbonization and desulfurization to desulfurization in the context of integrating coal chemical technology with green H2 without a water gas shift unit. The conventional Rectisol process, Selexol process, organic amine process, and sulfone-amine process have been reconfigured to form the R-Rectisol process, R-Selexol process, R-MDEA process, and R-Sulfolane-MDEA process to adapt to the shift of acid gas removal target. The removal efficiency of H2S and CO2, process energy consumption, and process cost are investigated to evaluate the technical and economic performance of the above four reconfigured acid gas removal processes. The results indicate that for the R-Recitsol process and R-Selexol process, both high pressure and low temperature increase the relative selectivity of H2S to CO2, thereby enhancing the selective removal of H2S. In contrast, for the R-MDEA process and R-Sulfolane-MDEA process, low pressure and low temperature improve the relative selectivity of H2S to CO2, thereby facilitating the selective removal of H2S. Under optimal operating conditions, the value of relative selectivity of H2S to CO2 of the R-Sulfolane-MDEA process is the largest, and the relative selectivity of H2S to CO2 of the R-Rectisol process, R-Selexol process and R-MDEA process are only 7.64 %, 12.85 %, and 68.40 % of the relative selectivity of H2S to CO2 of the R-Sulfolane-MDEA process, respectively. Compared to the other three reconfigured acid gas removal processes, the R-Sulfolane-MDEA process demonstrates superior advantages in both energy consumption and economic feasibility, provided that the H2S levels remain below 0.1 ppm in purified syngas. This study aims to enhance the decision-making process for gas purification methods in the coal chemical industry by considering integrated green H2 as an alternative to the water gas shift unit.

Critical analysis of analytical techniques developed for statins in biological fluids, environmental and fermentation samples.

Statins are the most prescribed drug for regulating the high cholesterol level in the blood, which can lead to severe complications, such as cardiovascular diseases and other health complications. A wide range of analytical techniques have been employed for the quantification of statins from various origins, including fermentation derived (lovastatin, pravastatin, and compactin), semi-synthetic (simvastatin), and synthetic (atorvastatin, rosuvastatin, and fluvastatin) routes. The presence of more than one structural form and structural analogue generated in the biosynthesis pathway, as well as reaction intermediates and macromolecules in the clinical sample, complicates the quantification of statins. Furthermore, significant concentrations of statins in environmental samples pose serious health and ecology hazards, and estimating statins in those diluted samples is extremely difficult. On the other hand, the: cost, accurate estimation of the desired one from other structural forms, sample complexity, time, limits of detection and quantification, were major criteria distinguishing the usability of each technique. As a result, the current manuscript focuses on analytical techniques such as molecular spectroscopy (normal and derivatives UV-Visible spectrophotometer), chromatography (TLC, HP-TLC, HPLC, GC, swing column, micellar, and supercritical fluid), mass spectroscopy (HPLC-MS/MS and GC-MS/MS), sequential flow injection, capillary electrophoresis, and cyclic voltammetry, as well as their: optimal operating conditions, limits of detection and quantification, advancements, and limitations. Furthermore, various online and offline sample preparations (precipitation, solid phase extraction, liquid-liquid extraction, and micellar extraction) have been highlighted as an essential pretreatment technique to avoid the interference caused by structural analogues and other macromolecules. The greener and more sustainable concepts used in analytical approaches for the quantification statins are also highlighted with current advancements.

Economical process design of reactive-extractive distillation combining variable pressure and heat integration for separating a ternary azeotropic mixture

Research and development of environmentally friendly processes that can generate significant profits has always been a focus of attention in pharmaceutical and chemical fields. In this study, a novel reactive-extractive pressure-swing distillation (REPSD) process was designed to separate tetrahydrofuran (THF)-methanol-water (TMW) ternary azeotropic mixtures. Ethylene oxide (EO) is reacted with water to form ethylene glycol (EG) in a reactive distillation (RD) column, which removes the water from the mixture. Then, the THF-methanol (TM) mixture is separated by extractive distillation (ED) and pressure-swing distillation (PSD). A multi-objective genetic algorithm (MUOGA) with total annual cost (TAC) and total capital cost (TCC) as objective functions, along with heat integration techniques were used to optimise the designed REPSD process. Optimal operating conditions to achieve maximum economic benefits were obtained for the process. Subsequently, the environmental benefits of the process were evaluated. The results showed that the proposed REPSD with heat integration can significantly reduce the TAC and CO2 emissions by 29.1 and 26.9 %, respectively, compared to existing optimal heat integration extractive distillation (HIED) processes. This study provides a new perspective on the separation and purification of ternary azeotropic mixtures.

Enhanced thermal modeling of power transformers: A comparison of bond graph and IEEE methods considering external environmental factors

The optimal and secure operation of electrical power system is largely affected by the reliability of power transformers in operational and economic terms. The continuous monitoring of power transformer is crucial for ensuring the quality of electrical power supply. Hence, the thermal modeling of power transformer is very essential to prevent their failure. This paper aims to design thermal models of operating power transformer by considering the two external influencing parameters of solar irradiation and wind flow. This paper presents a Bond Graph (BG) method-based design of thermal models for power transformer under operating condition by comparison with the results obtained from IEEE Std. based thermal models and also with the practical readings. The models have been implemented on a 5MVA, 33/11 kV power transformer running at 33 kV Substation, Sardarpura, Jodhpur, by initially considering the input hourly dataset of load and ambient temperature. The designed thermal models were subsequently updated by integrating the impact of solar radiation and wind incident on the surface of power transformer. Comparative study of all the designed thermal models has been done that indicates improved accuracy of the model if the impact of solar radiation and wind flow are included. Also, the effectiveness of the BG based thermal models in accurately predicting the system behaviour validates their applicability for real world engineering applications.

Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules.

Variable frequency interharmonic domain methodology for transient analysis and simulation of distributed generation systems

As the integration of renewable energy resources into the power grid increases, accurate modeling of variable frequency distributed generation systems becomes essential for ensuring grid stability, reliability, and optimal operation. Existing models often fail in effectively capturing the frequency dynamics of these systems, particularly when dealing with variable frequency sources. This gap hinders comprehensive analysis and the development of effective planning, control, and mitigation strategies.To address this challenge, this paper introduces a novel modeling methodology named here as the variable frequency interharmonic domain (VFID), which is based on the flexible extended harmonic domain (FEHD) approach. VFID dynamically adjusts the set of pre-selected frequencies within the state-space system to reflect changes in variable frequency sources. This method eliminates the need for post-processing routines, accurately representing both instantaneous values and frequency evolution of time-varying harmonics and interharmonics.Key results demonstrate that VFID offers significant improvements in simulation accuracy over conventional FEHD and PSCAD methods, making it a valuable tool for analyzing and simulating modern distributed generation systems. This advancement not only fills a critical research gap but also contributes to the development of more resilient and sustainable energy infrastructures.

Techno-economic optimization and feasibility of PCM-based seasonal thermal energy storage systems for district heating and cooling

Phase change materials (PCM) are an attractive seasonal thermal energy storage solution for load shifting due to relatively high energy density. Nevertheless, the choice of the right design parameters, such as storage size and phase-change temperature, is nontrivial, as these are tightly linked to the expected seasonal operation of the storage. This paper presents a combined design and operation optimization framework for sizing PCM-based seasonal thermal storage, which can capture dynamic behaviour of the storage in sensible and latent phases, and its impact on the efficiency of connected heat pumps and chillers. The proposed method, formulated as a mixed integer quadratically-constrained programming problem, was applied to a case study of heating-dominated district. It was found that the optimal PCM melting temperature equals to the heating demand supply temperature, thus removing the need of a heat pump to discharge the storage. The optimal operation of storage requires a full charging and discharging cycle of both latent and liquid phase. Higher CO2 emission price does not lead to a significant reduction of CO2 emissions, but the storage size does, leading to a higher heating coverage ratio of the storage, and consequently CO2 emissions and cost savings, which can be further enhanced with PV integration. The economic analysis indicates that, unless other benefits such as storage volume reduction are accounted for, PCM cost should drop by a factor 4 to make this solution economically viable for seasonal storage.