Optimal Operation Research Articles

Recovery of ammonia nitrogen from simulated reject water by bipolar membrane electrodialysis.

Ammonia monohydrate (NH3·H2O) is an important chemical widely used in industrial, agricultural, and pharmaceutical fields. Reject water is used as the raw material in self-built bipolar membrane electrodialysis (BMED) to produce NH3·H2O. The effects of electrode materials, membrane stack structure, and operating conditions (current density, initial concentrations of the reject water, and initial volume ratio) on the BMED process were investigated, and the economic costs were analyzed. The results showed that compared with graphite electrodes, ruthenium-iridium-titanium electrodes as electrode plates for BMED could increase current efficiency (25%) and reduce energy consumption (26%). Compared with two-compartment BMED, three-compartment BMED had a higher ammonia nitrogen conversion rate (86.6%) and lower energy consumption (3.5 kW· h/kg). Higher current density (15 mA/cm2) could achieve better current efficiency (79%). The BMED performances were improved when the initial concentrations of the reject water increased from 500 mg /L to 1000 mg /L, but the performance decreased as the concentration increased from 1000 mg /L to 1500 mg /L. High initial volume ratio of the salt compartment and product compartment was beneficial for reducing energy consumption. Under the optimal operating conditions, only 0.13 $/kg reject water was needed to eliminate the environmental impact of reject water accumulation. This work indicates that BMED can not only achieve desalination of reject water, but also generate products that alleviate the operational pressure of factories.

GeP3 monolayer as a promising 2D sensing materials in detecting SO2, H2S, SOF2 and SO2F2

The detection of SF6 decomposition components using gas-sensitive sensors is significantly important for characterizing internal insulation failures and assessing the operational status of SF6 gas-insulated equipment. In this paper, the adsorption properties of GeP3 monolayers for SO2, H2S, SOF2 and SO2F2 gases were investigated based on density functional theory. Four gas adsorption systems were constructed, and the adsorption mechanisms and sensing characteristics of GeP3 monolayers on target gases were investigated by calculating parameters such as adsorption energy, charge transfer, density of states, and recovery time, along with their potential application as resistive gas sensors and field-effect transistor sensors. It is demonstrated that GeP3 monolayers were suitable for the detection of SO2, H2S, SOF2 and SO2F2 gases, all of which exhibited good chemisorption with adsorption energies of −1.36 eV, −0.78 eV, −1.82 eV and −2.91 eV, respectively. The adsorption of SO2 and H2S is found to cause a significant change in the conductivity of the GeP3 monolayers, and desorption is achieved at the optimal operating temperature in only 54.428 s and 10.686 s, respectively. Also the adsorption of SOF2 and SO2F2 can make the work function of the GeP3 monolayers significantly larger. Consequently, the GeP3 monolayers have the potential to be used as a resistive gas sensor for SO2 and H2S gases, or as a field effect transistor sensor for SOF2 and SO2F2 gases. This study provides theoretical guidance for the development of GeP3-based sensors for monitoring the insulation status and operational conditions of SF6 gas-insulated equipment.

Optimization of Microbial Fuel Cell Operational Parameters for the Treatment of Brewery Sludge

AbstractThis study investigates the potential of a salt bridge‐mediated microbial fuel cell (MFC) for power generation and wastewater sludge treatment in breweries. Unlike traditional “one‐parameter‐at‐a‐time” methodologies, this study uses a three‐variable Box–Behnken design response surface methodology to optimize critical MFC operational parameters. The effects of parameters such as solution pH, salt bridge molarity, and temperature were studied in the range of 4 to 10, 1 to 5 M, and 20 to 45 g L−1. The optimum operating parameters were found to be solution pH of 5.853, salt bridge molarity of 3.343 M, and temperature of 32.5 °C for chemical oxygen demand and biological oxygen demand removal efficiencies of 92.485 % and 88.51 %, respectively. Temperature was found to be the most significant factor affecting the reactor's performance.

Matching Characteristics of Refrigerant and Operating Parameters in Large Temperature Variation Heat Pump

Characterizing the optimal operating parameters for a heat pump with a specific refrigerant is paramount, as it provides valuable guidance for refrigerant selection. The temperature mismatch between cold and hot fluids in the evaporator and condenser can lead to degraded thermal performance in heat pumps with large temperature variations. To address these two key issues, we selected several pure refrigerants with varying critical temperature levels for use in a large temperature variation heat pump configuration. The corresponding thermal performance was then investigated using the Ebsilon code under fixed temperature lift conditions as the operating temperature varied. It indicates that the maximum coefficient of performance (COP) is typically achieved when the deviation factors of temperature and pressure from their critical parameters fall within the ranges of 0.62~0.71 and 0.36~0.5, respectively. Our research recommends the binary refrigerant mixture of R152a/R1336mzz(z) (COP = 3.54) for the current operating conditions, as it significantly improves thermal performance compared to pure R1336mzz (z) (COP = 2.87) and R152a (COP = 3.01). Through research on the impact of the compositional ratio of R152a/R1336mzz(z) on the thermal performance of the heat pump, we found that that the optimal ratio of R1336mzz(z) component to R152a component is 0.5/0.5. This study offers valuable guidance for selecting the most suitable refrigerants for heat pumps in practical engineering design scenarios.

Carbon Dioxide Emission Characteristics and Operation Condition Optimization for Slow-Speed and High-Speed Ship Engines

Greenhouse gas emissions from ships are estimated to be approximately 1002 million tons per year; this is the largest carbon dioxide (CO2) emission source among nonroad transportation. Previous studies have generally estimated CO2 emissions using fuel- or power-based emission factors based on fuel consumption or engine power. In this study, CO2 emissions from vessels were measured using a portable emission measurement system. Emission characteristics were analyzed according to the vessel’s operation conditions and compared with the results of other studies. Generally, the higher the rpm value, the more CO2 is emitted, and the emissions at the maximum rpm differ depending on the type and size of the engine. In order to minimize the emissions by ships, those from high seas should be reduced rather than nearby ports. In addition, a method of establishing optimal operating conditions in consideration of economic and environmental perspectives was proposed. Fuel-based emission factors elicited in this study were constant regardless of engine rpm. The fuel-based emission factors of each engine were found to be similar at 3144.22 and 3150.58 kg-CO2/tonne-fuel. Therefore, distinguishing CO2 emission factors according to engine type is not necessary, and additional research is required to understand the emission factors of each fuel type.

Systematic optimization of off-grid green hydrogen production systems

In this work a novel mathematical programming formulation is presented for the simultaneous optimal design and operation of standalone, green hydrogen production systems. There is a pressing need to develop, test and commercialize technologies that can contribute in the medium term to our coordinated efforts to combat climate change. The system under study is inherently dynamic due to the stochastic and intermittent nature of meteorological data and there is a likelihood to overestimate its production capacity and economic performance. Failing to identify shortcoming in the design stage can result in expensive operational mistakes that will negatively affect the gathering race to build a massive new hydrogen economy. We present and demonstrate a systematic methodology that could be useful in decarbonizing our energy production systems by optimizing standalone, green hydrogen units and reducing the risk during their commercialization.

Enhancing energy efficiency of PCM wall in winter with novel PCM-DLCB coupling design

In order to enhance the thermal performance of the phase change material (PCM) wall during winter, a novel PCM and double-layer capillary bundles (PCM-DLCB) coupling wall is proposed. The PCM-DLCB wall utilizes the capillary bundle near the outer surface to capture solar energy during sunny winter days. The heat is then transferred to the capillary bundle near the inner surface through a circulating pump, promoting the melting of the PCM. Optimization and long-term operation comparison research were conducted using numerical simulation to investigate the potential of the PCM-DLCB wall to improve energy efficiency during winter. Five structural parameters, including the type of PCM, the position of the capillary bundle, the position and thickness of the PCM layer, the spacing of the capillary bundles, and the flow velocity in the capillary bundle, were selected as variables for structural and operation optimization. The optimization target was the accumulated heat gain from the inner surface of the wall. The research results indicate that: (1) The most suitable PCM is RT-18HC; (2) Placing the PCM layer between the double-layer capillary bundle yields better results compared to other arrangements; (3) Optimal energy saving is achieved when the outer capillary bundle is positioned 6 mm away from the outer surface and the inner capillary bundle is positioned 8 mm away from the inner surface; (4) The optimal thickness of the PCM layer is 10 mm; (5) The optimal flow velocity in the capillary bundle is approximately 0.04 m/s. Furthermore, long-term operation results based on a typical hot summer and cold winter climate zone reveal that the optimized PCM-DLCB wall provides a 7.80 % increase in indoor heat gain compared to an ordinary PCM wall during winter, and an 18.4 % increase compared to a wall without PCM.

Modeling Smart electrical microgrid with demand response and storage systems for optimal operation in critical conditions

This study examines the issue in standard and operational scenarios of microgrids that arise during critical conditions. Initially, the ideal energy storage size and discharge depth are identified for optimal microgrid planning under operating conditions. Subsequently, by utilizing the energy storage system and load response, the microgrid's vulnerability is reduced and the cost of load shedding is minimized when in critical conditions, leading to the microgrid being disconnected from the main network and operating in island mode. This model aims to analyze how the system performance is influenced by the presence of storage systems and load response programs in a numerical scenario, particularly during severe weather events. In addition, the study examines the role of advanced control algorithms and communication systems in optimizing the operation of the microgrid. By implementing smart grid technologies, the microgrid can better manage its energy resources, anticipate fluctuations in demand, and respond quickly to changing conditions. This proactive approach helps to ensure the stability and reliability of the microgrid, even in the face of unforeseen challenges. Overall, this research contributes valuable insights into the challenges and opportunities facing microgrids in both normal and emergency situations. By identifying the most effective energy storage solutions, load response strategies, renewable energy integration methods, and advanced control systems, the study aims to enhance the resilience, efficiency, and sustainability of microgrid systems in the future. The results of the proposed approach in critical operational mode represent the optimal condition of the electrical microgrid with minimum cost and load shedding considering storage systems and load response programs.

Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms

Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management.

Modeling and Optimization of Electrochemical Advanced Oxidation of Clopidogrel Using the Doehlert Experimental Design Combined with an Improved Grey Wolf Algorithm

In this research, the optimization of the electrochemical advanced oxidation treatment for the degradation of Clopidogrel was investigated. This study examined the influence of various experimental parameters including applied current, initial Clopidogrel concentration, and ferrous ion concentration by the use of the Doehlert design within a response surface methodology framework. The improved grey wolf optimizer was applied in order to define the optimum operating conditions. The monitoring of clopidogrel concentration during treatment revealed that complete disappearance of clopidogrel was achieved under an initial clopidogrel concentration of 0.02 mM, current intensity of 0.55 A, Fe2+concentration of 0.7 mM, and a reaction time of 20 min in a solution containing 50 mM Na2SO4 at pH 3. A quadratic polynomial model was developed, and its statistical significance was confirmed through the analysis of variance, demonstrating a high level of confidence in the model (R2 = 0.98 and p-value < 0.05). Furthermore, following electrolysis treatment for 480 min, the synthetic clopidogrel solutions underwent mineralization, achieving a 70.4% removal rate of total organic carbon. Subsequently, the applicability of the optimized process was tested on real pharmaceutical wastewater, and mineralization was investigated under the identified optimal conditions, resulting in a total organic carbon removal rate of 87% after 480 min of electrolysis time. The energy consumption for this system was calculated to be 1.4 kWh·kg−1 of the total organic carbon removed. These findings underscore the effectiveness and potential applicability of the electrochemical advanced oxidation for industrial wastewater treatment.

Thermal analysis on Ferro Casson nanofluid flow over a Riga plate with thermal radiation and non-uniform heat source/sink

Ferro hybrid nanofluids can be used in electronics and microelectronics cooling applications to reduce heat accumulation and efficiently remove surplus heat. These nanofluids aid to maintain optimum operating temperatures and reduce device overheating by enhancing the heat transfer rate. With this motivation, the aim of the present numerical analysis is to study the three-dimensional incompressible hybrid nanofluid flow over a slippery Riga surface by combining the Casson fluid model. Mathematical modeling is constructed with nanoparticles as Fe3O4 and CoFe2O4 with base fluid as water. The non-uniform heat source/sink and thermal linear radiation effects are taken into account with the Hamilton–Crosser thermal conductivity model. A system of nonlinear PDEs is produced by the proposed problem and then the relevant similarity variables are implemented to transform the set of partial differential equations and their accompanying boundary conditions into the coupled nonlinear differential equations with one independent variable. These modified ordinary differential equations (ODEs) were then successfully solved with the Runge–Kutta fourth-order method by combining via the shooting technique. With the aid of graphical representations, the effects of various influencing parameters were presented and analyzed comprehensively. Furthermore, the impacts of relevant parameters on heat transfer rates and shear stress are concisely discussed and illustrated in tabular forms. The significant findings include the enhancement of the radiation parameter increases the thermal boundary layer thickness and the thickness increases whenever surface experiences slippery conditions. The axial and transverse momentum of the fluid are controlled with the Casson parameter. An effective connection is noticed once the current numerical solutions are verified under particular conditions that were previously described.

Modeling Smart electrical microgrid with demand response and storage systems for optimal operation in critical conditions

Modeling Smart electrical microgrid with demand response and storage systems for optimal operation in critical conditions

Thermodynamic performance evaluation and optimization of a hybrid system integrating vacuum graphene-anode thermionic converters with direct carbon fuel cells

An efficient hybrid system integrating a direct carbon fuel cell (DCFC) with a vacuum graphene-anode thermionic converter (VGTC) is proposed for cogeneration. The VGTC efficiently recycles waste heat released from the DCFC and generate additional electricity, significantly improving energy utilization efficiency and economic performance. A comprehensive mathematical model is developed to quantitatively assess the thermodynamic performance of the hybrid system, taking into account the overpotential losses of the DCFC and the irreversible energy losses occurring within the system. The results show that at an operating temperature of 923 K, the hybrid system can achieve a maximum power density of 466 W/m2, which is approximately 1.34 times that of a single DCFC, indicating a significant improvement in output performance. Besides, the optimal operating region and parameter selection criteria for the hybrid system are determined using finite-time thermodynamic optimization theory. Furthermore, the effects of essential parameters on the output performance of the hybrid system, such as the operating temperature of the DCFC, the heat transfer coefficient, the Fermi level of graphene, the work function of the cathode, the thermal emissivity, and the reflectivity of the back mirror, are investigated. Finally, the comparative study shows that the proposed hybrid system outperforms other previously reported hybrid systems regarding output electric power and conversion efficiency due to the efficient high-grade waste heat recovery and energy conversion by the VGTC.

Wehnelt photoemission in an ultrafast electron microscope: Stability and usability

We tested and compared the stability and usability of three different cathode materials and configurations in a thermionic-based ultrafast electron microscope: (1) on-axis thermionic and photoemission from a custom 100 μm diameter LaB6 source with a graphite guard ring, (2) off-axis photoemission from the Ni aperture surface of the Wehnelt electrode, and (3) on-axis thermionic and photoemission from a custom 200 μm diameter polycrystalline Ta source. For each cathode type and configuration, including the Ni Wehnelt aperture, we illustrate how the photoelectron beam-current stability is deleteriously impacted by simultaneous cooling of the source following thermionic heating. Furthermore, we demonstrate usability via collection of parallel- and convergent-beam electron diffraction patterns and by formation of the optimum probe size. We find that usability of the off-axis Ni Wehnelt-aperture photoemission is at least comparable to on-axis LaB6 thermionic emission, as well as to on-axis photoemission [the heretofore conventional approach to ultrafast electron microscopy (UEM) in thermionic-based instruments]. However, the stability and achievable beam currents for off-axis photoemission from the Wehnelt aperture were superior to that of the other cathode types and configurations, regardless of the electron-emission mechanism. Beam-current stability for this configuration was found to be ±1% (one standard deviation from the mean) for 70 min (longest duration tested), and steady-state beam current was reached within the sampling-time resolution used here (∼1 s) for 15 pA beam currents (i.e., 460 electrons per packet for a 200 kHz repetition rate). Repeatability and robustness of the steady-state condition were also found to be within ±1% of the mean. We discuss the implications of these findings for UEM imaging and diffraction experiments, for pulsed-beam damage measurements, and for practical switching between optimum conventional TEM and UEM operation within the same instrument.

Wehnelt photoemission in an ultrafast electron microscope: Stability and usability

We tested and compared the stability and usability of three different cathode materials and configurations in a thermionic-based ultrafast electron microscope: (1) on-axis thermionic and photoemission from a custom 100 μm diameter LaB6 source with a graphite guard ring, (2) off-axis photoemission from the Ni aperture surface of the Wehnelt electrode, and (3) on-axis thermionic and photoemission from a custom 200 μm diameter polycrystalline Ta source. For each cathode type and configuration, including the Ni Wehnelt aperture, we illustrate how the photoelectron beam-current stability is deleteriously impacted by simultaneous cooling of the source following thermionic heating. Furthermore, we demonstrate usability via collection of parallel- and convergent-beam electron diffraction patterns and by formation of the optimum probe size. We find that usability of the off-axis Ni Wehnelt-aperture photoemission is at least comparable to on-axis LaB6 thermionic emission, as well as to on-axis photoemission [the heretofore conventional approach to ultrafast electron microscopy (UEM) in thermionic-based instruments]. However, the stability and achievable beam currents for off-axis photoemission from the Wehnelt aperture were superior to that of the other cathode types and configurations, regardless of the electron-emission mechanism. Beam-current stability for this configuration was found to be ±1% (one standard deviation from the mean) for 70 min (longest duration tested), and steady-state beam current was reached within the sampling-time resolution used here (∼1 s) for 15 pA beam currents (i.e., 460 electrons per packet for a 200 kHz repetition rate). Repeatability and robustness of the steady-state condition were also found to be within ±1% of the mean. We discuss the implications of these findings for UEM imaging and diffraction experiments, for pulsed-beam damage measurements, and for practical switching between optimum conventional TEM and UEM operation within the same instrument.

Multidimensional Scenario Calculations Using Cloud‐Based Co‐Simulation

AbstractAs part of the joint research project Carbon2Chem®, mathematical models are used to simulate operating scenarios of cross‐industry networks, in particular chemical plants in industrial environments with high CO2 emissions. In this context, a cloud‐based distributed co‐simulation is used to enable the parallelized simulation of a large number of different variants. This enables optimal design of the plant networks and the efficient determination of optimal operating conditions. The architecture of this approach is described, and its performance is examined and evaluated.

Multidimensional Scenario Calculations Using Cloud‐Based Co‐Simulation

AbstractAs part of the joint research project Carbon2Chem®, mathematical models are used to simulate operating scenarios of cross‐industry networks, in particular chemical plants in industrial environments with high CO2 emissions. In this context, a cloud‐based distributed co‐simulation is used to enable the parallelized simulation of a large number of different variants. This enables optimal design of the plant networks and the efficient determination of optimal operating conditions. The architecture of this approach is described, and its performance is examined and evaluated.

Collaborative optimization of distribution network and 5G base stations considering its communication load migration and energy storage dynamic backup flexibility

5G base stations have experienced rapid growth, making their demand response capability non-negligible. However, the collaborative optimization of the distribution network and 5G base stations is challenging due to the complex coupling, competing interests, and information asymmetry among different stakeholders. In this paper, a distributed collaborative optimization approach is proposed for power distribution and communication networks with 5G base stations. Firstly, the model of 5G base stations considering communication load demand migration and energy storage dynamic backup is established. Afterward, a collaborative optimal operation model of power distribution and communication networks is designed to fully explore the operation flexibility of 5G base stations, and then an improved distributed algorithm based on the ADMM is developed to achieve the collaborative optimization equilibrium. Finally, the effectiveness of the proposed distributed collaborative optimization model is validated by a modified IEEE 33-bus power distribution and communication networks with 5G base stations.

A two-stage decision-making approach for optimal design and operation of integrated energy systems considering multiple uncertainties and diverse resilience needs

The enhancement of energy system resilience from a planning perspective is crucial. In this study, a two-stage resilience optimization model is developed to determine the optimal additional configuration and scheduling plan for an integrated energy system, taking into account economic costs, loss of load, and various resilience indicators. Considering the uncertainty of disaster occurrence and component vulnerability, probabilities of multiple energy interruption scenarios are introduced, and a comprehensive system configuration optimization model targeting annual energy supply cost and loss of load is developed. Following which, accounting for the personalized energy demands of end-users, three resilience indicators, namely load importance, energy interruption duration, and the maximum degree of system damage, are proposed. According to the simulation results of an illustrative example, the proposed method effectively improve system resilience. Compared to the baseline solution from pure economic perspective, the total cost increases by 0.38 %, while the load loss decreases by 6.84 %. From the demand-side viewpoints, three resilience indicators are improved by 12.17 %, 11.8 %, and 20 %, respectively. Moreover, the capacity of renewable energy is recognized as the most critical factor determining the load loss over the planning period. Storage devices are more beneficial in reducing the duration of energy interruptions and lowering the maximum damage degree.

Stochastic Optimal Operation of SOP-Assisted Active Distribution Networks with High Penetration of Renewable Energy Sources

This paper introduces a mixed-integer convex model for optimizing the scheduling of soft open points (SOPs) integrated with energy storage (ES) in active distribution networks (ADNs) with high proportions of photovoltaic sources, designed to ensure zero risk of constraint violations. A stochastic optimization model for ADNs is proposed to maximize the benefits of SOPs while simultaneously minimizing system power losses, SOP power losses, voltage deviations, PV power curtailment, battery energy storage system (BESS) operation cost, and utility power purchase. Uncertainties in PV generation and load demand are considered by Monte Carlo simulation and k-means technologies. Finally, simulation cases from a 21-bus distribution network show that the curtailment of PV sources is minimized and the power fluctuations of the BESS are reduced in comparison to the case without SOP. Constraints in the nodal voltages, power outputs, energy balance, and power flow are all satisfied.