Optimal Operation Research Articles

A Case Study on Recycling Industrial Wastewater with Nanofiltration Membrane Separation Technology

As pressure on water resources intensifies and stringent regulations for groundwater and surface water are enacted, wastewater recycling has emerged as a key research objective for many enterprises. In this study, based on the actual wastewater discharged from Eternal Electronic (Suzhou, China) Co., Ltd., the performance differences in different membrane materials in treating this wastewater were analyzed and compared. The NF90 membrane was ultimately selected as the most suitable choice for treating this wastewater with optimal operating conditions of 150 psi, 25 °C, and 1 L/min, respectively. All the indices of wastewater treatment can satisfy the standard of circulating cooling water. In addition, this work is closely combined with the practical applications by using an HCl solution (pH = 3–4) to clean the fouled membranes, thus effectively solving the membrane fouling in the treatment process. This approach not only satisfies the environmental management requirements of enterprises but also provides valuable insights for similar wastewater treatment projects.

Optimal Operation of Multi-reservoir System Utilizing DEA, AIDE Algorithm and Flood Control Assessment by MCDM Approach

Optimal Operation of Multi-reservoir System Utilizing DEA, AIDE Algorithm and Flood Control Assessment by MCDM Approach

Effect of Speed Linear Decrease on Internal Flow Characteristics and Pressure Pulsations of Variable-Speed Pump Turbine in Turbine Mode

Pumped storage units often deviate from the optimal operating conditions in the process of regulating new energy fluctuations. To effectively improve the performance of the units, the variable speed of the units is one of the more feasible means at present. This paper focuses on the part-load condition of turbine operation, with an emphasis on the internal flow characteristics and pressure pulsation characteristics of the pump turbine during the linear reduction of the rated speed. It is found that the streamlines in the runner become turbulent in the process of speed reduction, forming a vortex at the inlet of the runner, and the vortex scale gradually increases with the speed reduction. The vortex rope in the draft tube undergoes three types of changes during the speed reduction: helical eccentric vortex rope, vanishing vortex rope, and columnar vortex rope. Before the speed change, the low-frequency components with high amplitude exist in each flow-passing part, but gradually disappear with the speed reduction. Except for the runner, the frequency affected by rotor–stator interference of each flow-passing part increases with the decrease of speed, and the growth is most obvious in the vaneless region. The findings of this research can serve as a valuable reference for the variable speed operation of pumped storage units.

Assessment of transmission network connection solutions based on HVAC for offshore wind

AbstractThe diffusion of offshore wind power plants (OWPPs), useful to fulfil renewable diffusion policy targets, implies several technological challenges for integration in power transmission network. The definition of the OWPP connection to the transmission network affects possible exploitation of the assets, involves an evaluation of active power losses and loadability and points out the need for compensation devices, in order to comply with connection rules fixed by the grid operator. This paper proposes a methodology to determine the most suitable electric connection for an OWPP based on high‐voltage alternating current (HVAC) cables, for providing indications for transmission system operators. In particular, different HVAC connection schemes for OWPPs are analysed, focusing on new 66 kV standard voltage and high‐voltage levels, providing technical insight on transmission cable optimal operation conditions. An economic analysis is carried out by proper estimation of construction costs and production forecast, accounting for active power losses and reliability impact. The most suitable connection solution depending on OWPP size and distance in Italian framework is evaluated by means of synthetic techno‐economic indicators, further analysing possible sensitivities of energy price and capacity factor.

Performance improvement with boosted intake pressure and hydrogen enriched biogas and producer gas-fuelled SI engine

The depletion of conventional fuels and rising energy demand necessitate alternative energy solutions, with biomass emerging as a viable resource. However, relying on a single renewable source poses challenges like inconsistent supply and reduced engine power output. This study numerically simulates a spark-ignition (SI) engine fueled by a triple fuel blend of producer gas (PG), biogas (BG), and hydrogen (HG). A comprehensive quasi-dimensional thermodynamic model, coded in FORTRAN, simulates SI engine performance by varying input factors such as biogas blending ratio, intake pressure, and late inlet valve closure timing (LIVC). This study introduces a novel approach to simulate the combined impact of triple fuel blending concentration, intake boost pressure, and inlet valve closure timing on SI engine performance and emissions. Additionally, it aims to identify optimal operating parameters to maximize power and efficiency while minimizing fuel consumption and emissions. Using response surface methodology and an analysis of variance regression model, results showed statistical significance at a 95 % confidence level. Optimal conditions were found at 82.99° aBDC for LIVC timing, 23.28 % biogas concentration, and 1.99 bar inlet pressure. Under these conditions, the engine achieved 2.39 kW brake power, 28.92 % brake thermal efficiency, 12,461 kJ/kWh brake-specific energy consumption, 0.081 % carbon monoxide, and 1640.36 ppm nitric oxide, with a composite desirability of 0.843. In triple fuel blend, while biogas concentration changes from 40 % to 23.28 % with increasing the intake pressure from 1 to 2 bar, BTE improves from 22.24 to 28.92 %. These results demonstrate that simulation and optimization can effectively predict SI engine performance with blended gaseous fuels.

Operating policies for robotic cellular warehousing systems

Robotic Cellular Warehousing Systems provide an innovative robot-to-goods picking approach designed to improve robot transportation efficiency, where robots move to pick items and transport the picked items to workstations. In this study, we investigate the optimal operating policies for such a system by comparing two picking strategies (pick-while-sort and pick-then-sort) and three robot-to-workstation assignment rules (random, closest, and dedicated). Specifically, we develop dedicated closed queuing networks to model robot-to-goods picking and estimate warehouse throughput under different policies through single-class and multi-class models. The effectiveness of these analytical models is validated through numerical simulations, with an average gap of 5.53% between simulation and analytical results. Additionally, we conduct a series of numerical experiments to examine the impact of various factors on warehouse performance, including the numbers of robots and workstations, robot capacity, order size, and sorting efficiency. Based on the experimental findings, we provide managerial implications that offer insights into optimizing resource allocation and system configuration. These insights enable warehouse managers to improve operational efficiency and overall performance.

Operation mode and scheduling plan optimization approach for multiple balancing zones in a distribution system

Modern power systems are developing rapidly, with distributed energy, energy storage devices, adjustable loads, and other flexible resources consolidated through microgrids, virtual power plants, and integrated source–network–load–storage systems. This consolidation under various balancing zone models facilitates synergistic operations and has become critical to enhancing distributed power consumption and ensuring the reliability of electricity supply. Therefore, in light of the challenges of inadequate economic efficiency, reduced accommodation of renewable energy, and poorer operational reliability in distribution networks, this study proposes a category selection and flexibility resource scheduling method that considers the differences in multiple balancing zone models and modes. Firstly, the approach establishes a multi-dimensional characteristic evaluation index and multiple balancing zone operation models. The characteristic evaluation indicators are then utilized to assess the unique properties of the balancing zone system and eliminate unreasonable operating modes. Finally, through analyzing the effectiveness and differences of various balancing zone operation modes, an optimal operation mode is selected, and a scheduling plan is formulated. We conclude that the scheduling plan optimization method considering the operation mode can realize a reasonable choice of operation modes and achieve benefit optimization.

Optimal operation of hydrogen-based multi-energy microgrid integrating water network and transportation sector

To address the rising demand for hydrogen energy and its reliance with the water sector, this study presents an optimal scheduling framework for a multi-energy microgrid (MEMG) that integrates electric, thermal, water, and hydrogen energy networks. To this end, a mixed-integer linear programming (MILP) model is formulated to minimize both operational costs and emissions. A bi-variate piecewise McCormick envelope technique is utilized to manage the non-linear constraints associated with the water network. The model also incorporates the transportation sector, including electric and hydrogen vehicles (EVs, HVs), with vehicle-to-grid (V2G) technology, and models their associated uncertainties using Monte Carlo simulation (MCS). Additionally, the sale of oxygen as a by-product of the hydrogenation process is also considered. The case study shows significant economic and environmental benefits, with a 29.66% cost reduction and 22.26% emissions decrease from water network integration. Oxygen sales further reduce costs by 14.19%, and V2G technology contributes an additional 2.35% cost and 6.01% emissions reduction. The proposed linear approximation method achieved superior performance, with a root mean square error (RMSE) of 0.72 and a relative error of 2.132%.

Efficient Removal of Cr (VI) and As (V) from Aqueous Solution Using Magnetically Separable Nickel Ferrite Nanoparticles

A promising nickel ferrite (NiFe2O4) NPs with excellent magnetic characteristics was synthesized using an aqueous-based reflux approach that eliminates the need for calcination temperature. The magnetic nanoparticles obtained were analysed using X-ray diffraction (XRD), Fourier-transform infrared (FTIR), and scanning electron microscopy (SEM). The analysis showed that synthesized nickel ferrite had a spherical shape with an average size ranging from 18 to 29 nm. The synthesized nickel ferrite is utilized for the removal of hexavalent chromium (Cr (VI)) and pentavalent arsenic (As (V)) ions from aqueous solution through adsorption method. The removal of Cr (VI) ions achieved a maximum efficiency of 65% after 120 minutes at a pH of 5, with a ferrite dose of 2 g/L and a concentration of Cr (VI) ions of 25 mg/L. while the removal of As (V) at a pH of 5, with a dose of 1 g/L of ferrite and an initial concentration of 25 mg/L of As (V) ions was 77% after 120 minutes. In addition, the data obtained from the isotherms of Langmuir (R2 = 0.99, 0.98), Freundlich (R2 = 0.86, 0.97) and Temkin (R2 = 0.83, 0.97). The adsorption of Cr (VI) ions is governed by a pseudo first-order kinetics process, whereas the adsorption of As (V) ions is governed by a pseudo second-order kinetics reaction. After five adsorption–desorption cycles at the same optimal operating condition, Cr (VI) and As (V) removal efficiency dropped from 65%, 77% to 25%, 30%, respectively.

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approch. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

Optimization of Process Design and Operating Parameters of H2S Removal Unit to Reduce Lean Amine Inlet Temperature of Amine Contactor at Upstream Oil and Gas Subsidiary SI

Upstream Oil and Gas Subsidiary SI is a natural gas processing company that operates an H2S removal unit to convert natural gas rich in CO2 and H2S into sweet gas. The main problem of this unit is the high temperature of lean amine entering the Amine Contactor. The purpose of this study is to determine the factors of high lean amine temperatures, evaluate the lean amine cooler, amine regenerator overhead cooler, and plate exchanger performance, and determine the optimal process design configuration and operating parameters. The method used is the simulation of the H2S removal unit with Aspen HYSYS, followed by a comparative analysis between simulation data and equipment design data. The independent variables of this study include Reboiler duty, reflux ratio, and heat transfer area in the Plate Exchanger, with the main dependent variable being lean amine temperature. The results showed that the high lean amine temperature was caused by a decrease in the performance of the Lean Amine cooler and amine regenerator overhead cooler, as seen from the UA and LMTD values of the simulation results which were smaller than the design. In contrast, the Plate Exchanger still functions well with a UA value greater than the design. Optimization was carried out by adjusting the process design and operating parameters of the H2S removal unit. The optimized design involves bypass reflux from the regenerator to the rich amine stream entering the rich/lean amine exchanger and increasing the heat transfer surface area of the plate exchanger to 62.17 m². The influential operating parameters are reboiler duty, liquid flow rate to the mixer, and plate exchanger heat transfer surface area. Optimal operating conditions were achieved at a Reboiler duty of 1,642 kW, a liquid flow rate of 1.4 m³/h, the reflux to Mixer ratio is 100%, and a heat transfer surface area of 62.17 m². In addition, it can be concluded that the optimization of process design and operating parameters successfully reduced the lean amine inlet temperature of the Amine Contactor.

Impact of different regulatory approaches in renewable energy communities: A quantitative comparison of european implementations

Recently, the uptake of renewable energy has surged in distribution networks, particularly due to the cost-effectiveness and modular nature of photovoltaic systems. This has paved the way to a new era of user engagement, embodied by individual and collective self-consumption, and promoted by the EU Directive 2018/2001, which advocates for the establishment of Renewable Energy Communities. However, the transposition of this directive varies across Member States, resulting in specific rules for each country. In this work, the impact that different energy sharing models have on the same community is quantitatively assessed. The policy analysis focuses on the regulation of two countries, Italy and Portugal, chosen for the specular ways in which their models operate, respectively virtually and physically. The analysis is supported by a suite of tools which includes two optimization problems for community's operations, one for each analysed regulation, and a set of consumer protection mechanisms, to ensure no member is losing money while in community. Results demonstrate that the sharing model impacts community's optimal operations, optimal battery size and configuration, and members' benefit. As these models are sensitive to different variables, personalized interventions at national level are required.

Artificial Intelligence based Emission and Performance Prediction, and Optimization of HHO-Blended Gasoline SI Engine: A Sustainable Transition

In striving for sustainable alternatives to gasoline, Oxyhydrogen (HHO) has emerged as a promising substitute for Internal Combustion Engines (ICEs). HHO blends not only improve engine efficiency but also reduce harmful emissions. On-site, HHO utilization in the engine, eradicates low energy density and storage challenges. The current study combined cutting-edge machine learning (ML) techniques like Artificial Neural Network (ANN) and Gradient-based optimization to effectively utilize HHO with gasoline. Experimentation involved a single-cylinder spark ignition (SI) engine fueled by varying HHO-gasoline blends across different loads and speeds. Iterative tuning of the loss function led to the identification of the optimal architecture, denoted as 2HL-10N (2 hidden layers with 10 neurons each), with impressive correlation coefficients (0.99481 for training, 0.9781 for validation, 0.96914 for testing, and overall, 0.98819). Subsequently, ANN led Gradient-based optimization unveiled key performance metrics along with emissions. Upon implementing optimized conditions (HHO: 3.78 l/m, load: 100%, and 3465 rpm), notable enhancements were observed. The torque and efficiency increased by 11.8%, and 7.1%, respectively. Furthermore, brake-specific fuel consumption, carbon monoxide, and hydrocarbon emissions showed a reduction of 11.5%, 27.1%, and 36.6%, respectively. ANN based optimal engine operation revealed HHO as a potential replacement for conventional gasoline.

The energy management strategy of two-by-one combined cycle gas turbine based on dynamic programming

The complexity and nonlinearity of components in large-scale thermal facilities have resulted in a lack of recognized energy management models, and simple rule-based energy management strategies are still the main approach, which reduces their operating efficiency. In this study, a dynamic programming (DP) method for globally optimal power distribution and operation mode decision for two-by-one combined cycle gas turbine is proposed. First, the energy management model of system is established. Then the drum pressure of the heat recovery steam generator, which indicates the thermal energy storage of the system, is chosen as the state variable, while the control variables are the gas turbine power, turbine power and operation mode. In addition, the system response time is considered to re-evaluated the mode switch command. The simulation results show that the DP optimizes the thermal storage management, which allows the gas turbine to run in the high-efficiency operating range for a longer time. The DP-based strategy saves 6.25%, 5.89%, and 4.92% of fuel at initial drum pressure 8MPa, 9MPa, and 10MPa, respectively, compared to the rule-based strategy. The results of this study can be used as a benchmark to evaluate online energy management strategies in future work.

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

In a power-hydrogen coupled integrated energy system (PHCIES), hydrogen fueling stations (HFSs) with solar photovoltaic (PV) systems are crucial devices to support hydrogen demand and maintain a stable and near-zero pollutant emission-based PHCIES operation by producing a pollutant-free hydrogen. However, optimal operation management of HFSs is challenging because the electrolyzers, compressor, hydrogen storage system (HSS), and fuel cell in HFSs are interconnected and change dynamically under various operational environments. To resolve this issue, this paper presents a safe deep reinforcement learning (DRL)-assisted two-stage framework that ensures a reliable and economical PHCIES operation. In the first stage, day-ahead operational schedules of stand-alone PV systems, PV-enabled HFSs, on-load tap changers, and capacitor banks with hourly resolution are coordinated to minimize the system operation cost, electricity arbitrage cost, PV curtailment cost, and real power loss by solving a Volt-VAR control (VVC) optimization problem. In the second stage, a safe DRL algorithm with a 15-min resolution is employed to minimize the real power loss and maximize the HFS profit by rescheduling the reactive power of stand-alone PV systems and real/reactive power and hydrogen of HFSs. The proposed self-tuning adaptive safety module integrated in the DRL method ensures no violations of HSS’ state-of-hydrogen (SOH) and voltage magnitude in the PHCIES during the training process. Numerical examples conducted on a PHCIES (IEEE 33-bus system coupled with two PV-enabled HFSs) demonstrate the effectiveness of the proposed framework in terms of training convergence, SOH/voltage violation, real power loss, and profit of HFSs.

Design and Implementation of a Cost-Effective, Safe, and Precise Lean-Rich Generating System for NOX Storage Reduction Catalyst Evaluation: Performance Analysis, Statistical Modeling, and Multiple Response Optimization – A Guideline for Future Research and Application

Mixing is a critical factor in chemical reaction engineering, particularly in the development of new catalysts for pollutant removal, such as NOx storage reduction catalysts. Comprehensive performance evaluation in laboratory experiments requires high-quality test gases. Several techniques have been developed for generating these gases, each with specific advantages and limitations. This research presents a simple, safe, and cost-effective gas generation system for laboratory applications, including evaluating adsorbents and catalysts, particularly NOx storage reduction catalysts, and for toxicological studies using precise dynamic methods like gas stream mixing and evaporation. Components of the dynamic system—including gas cylinders, connections, mixing chambers, rotameters, the quartz reactor with preheating chamber, bubbler systems, and tubular heaters—are comprehensively described. Statistical modeling and optimization were employed to determine the optimal operating conditions. The optimal conditions were identified as NO (924 ppm), O₂ (15%), CO (9999 ppm), and a space velocity of 105,921 h⁻¹. Additionally, the system's ability to generate various mixtures of gases (NO, O₂, and CO) and vapors (toluene and water) was assessed. The system was further evaluated under fan-on and fan-off modes to assess its performance under varying turbulent conditions. A detailed analysis was performed to determine the relative errors, time to reach steady-state conditions, and actual concentrations produced under different expected concentrations (NO: 100-200 ppm, O₂: 3-15%, CO: 500-10000 ppm, toluene: 64-8999 ppm, relative humidity: 0.14-22.69%), space velocity (70,000-140,000 h⁻¹), bubbler flow rates (10-600 mL/min), and saturator temperatures (5-20°C). Fundamental equations for calculating conversion, selectivity, and yield of a component of interest, carbon-messing, and carbon balance in the mixture are also provided.

Stochastic assessment of voltage sags and techno-economic optimization

Power quality has become an essential concern for industries and domestic energy consumption with the development of sophisticated equipment like micro-electronic devices. When a power quality event happens, such as voltage sag, financial losses for industries increase tremendously. Therefore, voltage sag evaluation becomes crucial for predicting the number of events and severity of voltage sag events of the electrical network. Industries can determine the mitigation investment needed for optimal operation using the knowledge obtained through voltage sag analysis method. Thus, the first part of the study addressed the development of a stochastic assessment method for voltage sag at an interested bus in the power system network, which was an initial objective of the study. The second part of the research investigates dynamic voltage restorer (DVR) control techniques, as well as the necessary capacitance and energy storage dimensions. The studies also recommend a technique for economic evaluation that emphasizes the expense of voltage sag events as well as the cost of investment in DVR with and without voltage sag events. The size of DVR and economic assessment model established in these studies enables industries to rate the cost of voltage sag and the total investment in mitigation devices.

Strategic optimization of dual-fuel diesel/gas engines by numerical approach for environmental and economic benefits

The study introduces a framework for optimizing eco-friendly dual-fuel engines, combining CFD and ANN, to reduce carbon emissions and improve sustainable transportation.We achieved optimal performance with a 52–65 % NG substitution ratio. SOI timing had a big effect on NOx emissions; later injection decreased NOx formation because it made the mixture more homogeneous. We developed a radial basis function neural network model to predict engine performance and emissions with 99 % accuracy. The study examines the effect of fuel droplet spraying on cylinder walls on engine performance and emissions, utilizing response surface methodology to create engine maps. Numerical experiments revealed that a lowered natural gas replacement ratio of less than 50 % resulted in decreased flame propagation and the attainment of a burn mixture mode. The decrease in flame propagation speed during CA10 led to a fivefold increase in CO emissions. As the NG substitution ratio exceeded 50 %, the air/fuel ratio neared equilibrium, resulting in a reduction in NOx emissions and in-cylinder combustion temperature. Nevertheless, the highest RoHR level decreased by 25 %. The downward trend proceeded until the natural gas substitution ratio reached to 90 %. The TOPSIS method was utilized to identify optimal operating conditions for DFDI engines, offering valuable insights for efficiency and emissions reduction.

Industrial Cooling Systems a Comprehensive Review of Technologies, Challenges, and Future Directions

Abstract Industrial cooling systems are critical components in a wide range of sectors, including manufacturing, energy production, chemical processing, and data centers. These systems play a vital role in maintaining optimal operating temperatures, enhancing equipment efficiency, and ensuring safety. This review paper provides a comprehensive analysis of the current state of industrial cooling technologies, categorizing them into air cooling, liquid cooling, phase-change cooling, and advanced cooling methods such as thermoelectric and magnetic refrigeration.

Real time monitoring of Rn-222 in workplaces and estimation of working time correction factor

The potential of electronic monitors for evaluating radon exposure is still not fully harnessed, and tests under real conditions are scarce. Ten workplaces were continuously monitored for a year with the radon measurement network of Sofia University, with calibrated and metrologically assured RadonEye +2 monitors. In 7 of the workplaces the average activity concentration of radon during the working time was significantly lower than the total time average. This difference is attributed to behavioral patterns. The ratio of the working time and the total time average (called working time correction factor kWT) for the whole year ranged from 0.71 to 0.98. Its weekly values varied significantly with coefficients of variation between 9 and 28%. Therefore, using short term estimates of kWT to correct the total time radon average determined by passive detectors could lead to bias.The workplaces also exhibited different seasonal radon patterns, although most were offices with similar schedules. In most pairs of workplaces the monthly averages were correlated very weakly or even negatively. This indicates that seasonal radon variations could be specific to each workplace. The value of kWT also varied with the season, suggesting that seasonal variations of radon during working hours might differ from those observed with passive integrating detectors.Overall, long-term follow-up by metrologically assured electronic monitors could help to improve and personalize the estimate of radon exposure of workers. These monitors could potentially support smart anti-radon systems with optimal operation schedules, thereby contributing to the reduction of the energy impact of buildings.