Optimal Operation Research Articles

Enhancing Oil Recovery in Eagle Ford Shale: A Multiscale Simulation Study of Surfactant Huff ’n’ Puff Methodology

In this paper, we present a simulation case study of a surfactant huff ’n’ puff pilot in the black oil window of the Eagle Ford (EF) Shale. The target horizontal well, which had been depleted for nearly 8 years, underwent stimulation via a surfactant huff ’n’ puff treatment. The surfactant was selected through laboratory screening using reservoir rock and fluid samples. After a 17-hour injection and a 1-month shut-in period, the well’s production increased fivefold from the baseline oil rate, sustaining incremental oil production for at least 2 years. The surfactant enhances oil recovery by altering rock wettability toward a more water-wet state and moderating oil/water interfacial tension (IFT). This process is modeled by surfactant adsorption in the simulator, indicating the degree of dynamic changes in relative permeability (krl) and capillary pressure (Pc) curves. We propose a comprehensive workflow comprising three stages: development of core-scale and field-scale models, sequential model calibrations, and multiobjective optimization to integrate laboratory measurements and field data from this pilot into multiscale numerical simulations. By matching oil recoveries from imbibition experiments on the core model and field production histories on the field model, krl and Pc profiles of two extreme states, basic reservoir properties, and additional reservoir properties altered during huff ’n’ puff operations are characterized. The matched core model reproduces a 15.1% incremental oil recovery for surfactant-assisted spontaneous imbibition (SASI) process relative to pure brine imbibition process. The matched reservoir model predicts the surfactant huff ’n’ puff treatment increases the oil production by 21.9% relative to water huff ’n’ puff treatment and by 52.9% relative to primary depletion for a 4-year period. The calibrated reservoir model also serves as a base case for optimizing well operation schedules through the implementation of a multiobjective genetic algorithm. The surfactant injection rate, injection time, and well shut-in time of the base case are varied to achieve higher oil production and reduced surfactant usage. Statistical analysis of eight trade-off cases indicates that optimal well operations, compared with existing practices, frequently involve increased injection rates [16.6–18.9 barrels per minute (bpm)], shorter injection periods (10–11.3 hours), and prolonged shut-indurations (49–65 days). This workflow offers valuable insights into surfactant huff ’n’ puff treatments for unconventional reservoirs, thereby facilitating the optimization of well operations and maximizing tertiary oil recovery.

Assembly and operation optimization of proton exchange membrane water electrolyzer for performance enhancement

Abstract Proton exchange membrane water electrolysis (PEMWE) for hydrogen production possesses wide-ranging and rapid dynamic response capabilities, offering promising applications in the consumption of new energy sources and the dynamic balancing of power grids with a high proportion of renewable energy. The assembly and operating conditions of PEMWE significantly affect its performance. Therefore, thoroughly studying and understanding the influence of PEMWE’s assembly and operating conditions on water electrolysis performance are crucial for enhancing PEMWE’s performance and promoting its applications. In this research, the influence of installation preload torque, feedwater flow rate, and operating temperature on the performance of the electrolyzer, including the electrochemical active specific surface area (ECSA), high-frequency resistance (HFR) and various types of polarizations (including activation polarization, ohmic polarization, and mass transfer polarization) during the operation of the electrolyzer, were detailedly investigated. The results showed that the optimal preload torque and operating temperature for the electrolyzer were 3 Nm and 80°C. Under these conditions, an optimized PEMWE performance would be achieved by ensuring that the feedwater flow rate exceeded the water consumption during electrolysis.

Optimal economic operation of smart home devices based on home microgrids and time-of-use tariffs

Abstract In this paper, a two-layer model is proposed for smart devices in a home energy management system in order to optimize residential energy use under time-of-use tariffs and home microgrids. Home smart devices are modelled based on their power consumption attributes and building thermal resistance. An HVAC-transferable device cooperative operation strategy is introduced to construct the economic optimization of the upper-level HVAC operation and the lower-level transferable device operation. This strategy reduces home power costs while maintaining user comfort. By iteratively solving the two-layer model, the optimal power consumption scheme for home smart devices is determined.

Numerical analysis on the performance of sodium chloride solution evaporative crystallization system driven by high-temperature heat pump

In the context of sustainable development, heat pump technology is increasingly recognized as a key method for enhancing the efficiency of evaporative crystallization processes. In this study, we propose a high-temperature heat pump evaporative crystallization system to facilitate the recycling of waste heat from secondary steam in low-vacuum and low-temperature evaporative crystallization, thereby achieving the cyclic utilization of condensation heat from secondary steam. The primary objective of this research is to develop a comprehensive mathematical model for the HPC system that investigate its operating performance under varying conditions, with the aim of promoting its practical application. The results demonstrate that by adjusting operating pressures, the temperature of the secondary steam can be controlled, allowing for regulation of the main particle size of the product. Specially, when maintaining the flow rate of the raw material liquid at 90 % of design condition, it is possible to achieve a maximum main particle size of 2.8 mm. Additionally, fluctuations in the flow rate of the raw material liquid significantly impact the cycle rate, with the optimum operating flow rate range identified as 75 % to 110 % of the rated value. The coefficient of performance of the system can achieve 5.85 under design conditions with a concentration of 4 % and flow rate of 310 kg/h. This research provides a theoretical foundation for the practical operation and commissioning of the HPC units.

Knowledge-Data Driven Optimal Control for Nonlinear Systems and Its Application to Wastewater Treatment Process.

Optimal control is developed to guarantee nonlinear systems run in an optimum operating state. However, since the operation demands of systems are dynamically changeable, it is difficult for optimal control to obtain reliable optimal solutions to achieve satisfying operation performance. To overcome this problem, a knowledge-data driven optimal control (KDDOC) for nonlinear systems is designed in this article. First, an adaptive initialization strategy, using the knowledge from historical operation information of nonlinear systems, is employed to dynamically preset parameters of KDDOC. Then, the initial performance of KDDOC can be enhanced for nonlinear systems. Second, a knowledge guide-based global best selection mechanism is used to assist KDDOC in searching for the optimal solutions under different operation demands. Then, dynamic optimal solutions of KDDOC can be obtained to adapt to flexible changes in nonlinear systems. Third, a knowledge direct-based exploitation mechanism is presented to accelerate the solving process of KDDOC. Then, the demand response speed of KDDOC can be improved to ensure nonlinear systems with optimal operation performance in different states. Finally, the performance of KDDOC is validated on a simulation and a practical process. Several experimental results illustrate the effectiveness of the proposed optimal control for nonlinear systems.

Enhanced sensitivity and selectivity of ZnSe/PANI composite for low ppm level room temperature detection of NO2

The utilization of n-p composite-based gas sensors has garnered substantial interest within the field of gas sensing. This heightened attention can be attributed to the remarkable band alignment of the constituent materials which results in superior charge transfer rate, increased gas interaction sites and reduced optimum operating temperature. In this work, n-ZnSe/p-PANI composites were prepared by simple hydrothermal technique. The obtained X-ray diffraction pattern confirms the phase formation ZnSe, PANI and ZnSe/PANI composites. The pure ZnSe exhibits a sensing performance at a higher operating temperature of 100 °C, whereas ZnSe/PANI composite sample demonstrates an improved sensing response at 30 °C. Notably, the 20 wt.% composite sample (ZnSe–P2) achieved a maximum sensing response of 77 % towards 20 ppm of NO2 gas molecule. Additionally, the sensor exhibits the response time (Tres) of 112 s and recovery time (Trec) of 648 s, at an operating temperature of 30 °C. It also shows better stability, reproducibility and specific selectivity towards NO2 gas molecule. The superior sensing behaviour of the ZnSe-P2 sensor at 30 °C can be ascribed to the development of a depletion region in the interface of ZnSe/PANI composites, which improved the charge transfer rate and increased the number of reactive sites. Therefore, the formation of n-p inorganic-organic composite strategy offers an effective approach for detecting NO2 gas molecules at lower temperature of 30 °C.

Instrumentation in Tractor for Evaluating Performance of a Tractor Drawn Rotavator

Performance measurement of agricultural machinery is essential for enhancing their efficiency for which they are operated. The performance related data helps the operator to select optimum operating parameters which reduce fuel consumption and increase field productivity. This study was aimed to develop various sensing units and install them on the tractor for evaluating performance of a tractor drawn rotavator. A microcontroller based embedded system was developed for continuous recording of wheel slip, actual forward speed, throttle opening, engine speed, velocity ratio, draft and power take-off (PTO) torque related data while carrying out tillage operation with rotavator. The wheel slip (from 1.5% to 35%) and actual forward speed of the tractor (up to 6 km h-1) was measured using inductive proximity sensors. For measuring throttle opening (0 to 100%), engine speed (750 to 2500 rpm), draft (65 to 350 kg) and PTO torque (up to 420 N-m) values, a potentiometer, magnetic pickup sensor, S-type loadcell and PTO torque transducer, respectively, were used. For computing velocity ratio, peripheral speed of rotavator was measured using PTO speed data received from PTO torque transducer, and actual forward speed of tractor was measured using inductive proximity sensor. The developed sensing units were calibrated under both dynamic and static conditions, and were also verified in laboratory and field conditions. Results showed that output signals received from the sensing units varied linearly with applied load. The mean absolute percentage error (MAPE) between measured and actual values was found to be below 5%. From the measured parameters, specific energy requirement and total power required for carrying out tillage operation could be computed. The power requirement was found to increase with increase in both gear and throttle settings. The specific energy requirement was minimum when rotavator was operated in L2 gear of the tractor. The minimum specific energy of 21 Wh m-3 was found at L2 gear and 75% throttle opening.

Transforming waste to purity: 3D electro-Fenton process boosted with pistachio shell-derived iron-biochar electrode for methyl violet 2B dye catalytic removal

Traditional degradation methods with typical catalysts which have limited negatively charged surfaces are not effectively degrade cationic dyes. A multi-functional iron-biochar was prepared from waste pistachio shells (Fe@PSB) that offers enhanced dye removal due to abundance of negatively charged sites for the degradation of the carcinogenic dye known as methyl violet 2B (MV 2B) in a cutting-edge three-dimensional electrochemical Fenton system (3DEF) with graphite (GP) cathode and Titanium (Ti) anode (3DEF-Ti/GP). Catalytic properties of Fe@PSB were explored for MV 2B dye degradation at broad acidic pH range. At optimum operating conditions of the current of 2.0A, pH 5.0, and Na2SO4 dose of 0.2M, nearly 98.8% of 10mg/L MV 2B dye removal was attained in 60min of electrolysis time. Commercial particle electrodes did not perform as well for the MV 2B dye degradation. The catalytic mechanism of 3DEF-Ti/GP system was investigated by quenching experiments which revealed that the hydroxyl radical (•OH) was responsible for the oxidation of dye molecules. Fe@PSB significantly performed up to four successful cycles. Liquid chromatography-mass spectrometry analysis revealed that de-methylation was the primary degradation pathway. This unique approach not only valorizes agricultural waste but also paving the way for greener environmental solutions.

Comprehensive performance analysis and optimization of an ORC-HDH system for power and freshwater production driven by marine exhaust gas

Freshwater serves as a vital energy source for ships, underscoring the paramount importance of research into desalination systems in facilitating diverse and reliable energy supply for maritime vessels. Aiming at numerous waste heat of marine engines and the shortage of freshwater resources on board, this paper designs an innovative waste heat recovery (WHR) system. It includes an organic Rankine cycle, a compression refrigeration cycle, and a humidification-dehumidification (HDH) desalination system to recover waste heat from exhaust gases in cascades, which can effectively produce power, cooling, and freshwater, respectively. A parametric study of the co-generation system in terms of the freshwater production, gained output ratio (GOR), cost per unit of freshwater production, net power output, and per unit of output power cost, are conducted to identify the key operating parameters. A set of operating parameters is determined for the HDH system with a temperature of the humidifier outlet of 358.15 K and a mass flow rate ratio (MR) of seawater to humid air of 4.5. The effect of the relative humidity and temperature of the humid air, as well as the salinity and temperature of the seawater, on the performance of the HDH subsystem are analyzed. And the optimal operating conditions of the co-generation system are obtained through multi-objective cuckoo search optimization. The results demonstrate that the co-generation system can produce an output-power of 1479.8 kW, 500 kW, and 0.49 kg/s for cooling and freshwater production by using the working-fluid R365mfc/Cyclopentane with a mixing ratio of 33:67 under optimal conditions. The GOR and cost per unit of HDH subsystem are 1.62 and 1.70 $/m3, with a capital payback period of 4.18 years. The equivalent output-power of the co-generation system is 9.5 % higher than that of an ORC system.

Optimal Operation Framework of the Hydrogen-electric Integrated Energy System by Considering the Power to Hydrogen Efficiency Factor

Optimal Operation Framework of the Hydrogen-electric Integrated Energy System by Considering the Power to Hydrogen Efficiency Factor

Two-stage robust optimization for optimal operation of hybrid hydrogen–electricity storage system considering ladder-type carbon trading

The prominent problems of renewable energy curtailment and its uncertainty have become a hot topic. To the end, with consideration of environmental friendliness, energy utilization efficiency and operation cost, this paper proposes a hybrid hydrogen–electricity storage system (HHES) operation framework comprising assorted types of coupling devices and carbon capture system (CCS) under ladder-type carbon trading (LCT) mechanism. By considering energy balance constraints, equipments’ operation constraints and erratic rates of RESs and multiple loads, a scheduling model with source-load uncertainties is developed with the goal of minimizing the system’s operation cost and carbon emission. Then, a two-stage tri-level robust model is built to find the optimal scheduling plan under multiple uncertainties given the various sources and loads. The first level is to make an optimal dispatch plan and the second level is to find the worst scenario under the given uncertainty set. The third level aims at enhancing renewable energy accommodation rate and reducing load shedding rate in the worst case. Since the model is formed as a tri-level mixed integer optimization problem, the Column and Constraint Generation (CCG) method is adopted to achieve the optimal result. Finally, numerical analyses are performed. The results show that the proposed approach realizes a flexible dispatch of multiple energy and a high utilization level of multiple storages. Furthermore, this paper studies the influence of uncertainty and uncertainty budget parameters with the comparison of the deterministic model and robust models with different uncertainties. The results show that the proposed HHES has strong robustness and can provide a good reference for energy dispatch.

Optimal operation of multi-energy carriers considering energy hubs in unbalanced distribution networks under uncertainty

This article presents a two-stage stochastic programming model to address the dispatching scheduling problem in an energy hub, considering an unbalanced active low-voltage (LV) network. A three-phase version of the second-order cone relaxation of DistFlow AC optimal power flow (AC-OPF) is employed to incorporate unbalanced network constraints, while the objective minimizes the Local Energy Community (LEC) operational cost. The model results have been validated using OpenDSS, encompassing energy losses, voltage levels, and active/reactive power. Likewise, a comparative analysis between the three-phase model and the traditional single-phase model, using a modified version of the IEEE European LV Test Feeder as a case study, reveals interesting differences, such that the single-phase model underestimates voltage limits during photovoltaic (PV) system operation and overestimates energy purchased from the main grid, compared with the three-phase model. Similarly, the comparison results reveal that discrepancies between the single and three-phase models intensify as the power injected from PV systems rises. This notably impacts the total energy purchased from the grid, battery operation, and the satisfaction of thermal consumption through electricity. Finally, while the three-phase model offers valuable insights into security levels for voltage and grid energy purchase, its longer computational time makes it more suitable for strategic use rather than daily operational frameworks.

Study of ship-based carbon capture optimization considering multiple evaluation factors and main engine loads

Ship-Based Carbon Capture (SBCC) technology presents a promising approach to reduce greenhouse gas emissions in the marine transportation. Solvent-based carbon capture has emerged as a leading technology for SBCC, primarily due to its low retrofit costs and effectiveness in treating flue gas with low carbon content. However, challenges remain in optimizing system operation efficiency under the variable conditions of ship main engine loads and in understanding the mechanisms by which process parameters influence key evaluation factors. Consequently, this study established a pilot-scale experimental platform for solvent-based SBCC and developed a carbon capture model based on it. The influence of process parameters, such as main engine exhaust flow rate and liquid/gas ratio (L/G), on the evaluation factors was thoroughly explored. With the utilization of Shapley Additive Explanations (SHAP) analysis to quantify the influence of process parameters on the evaluation factors, a multi-objective optimization equation is proposed. The combination of machine learning and optimization algorithms offers an efficient approach for determining the optimal process of the SBCC system under different main engine loads. The research results show that the main engine exhaust flow rate is the most important process parameter affecting the carbon capture rate (CR), with an influence ratio as high as 64.33%. As the main engine exhaust flow rate increases, the maximum CR decreases from 97.84% to 61.13%. Although the extreme value of the regeneration energy consumption (REC) also decreases from 6.66 to 4.42 GJ/t CO2, the solvent flow rate's influence on REC is also significant, with influence ratios of 38.49% and 35.53%, respectively. A data-driven approach examined the optimal operating conditions at eight main engine loads, achieving a carbon capture rate prediction error of only 1.42% and a maximum REC error of only 0.55 GJ/t CO2. This study provides essential guidance for assessing the suitability of SBCC systems.

Importance de l’analyse vibratoire sur la maintenance préventive des réducteurs planétaires dans une cimenterie: « cas de la cimenterie de Lukala dans la province du Kongo Central en R.D. Congo »

Planetary gearboxes are essential for reducing the rotational speed of electric motors to a suitable level for cement production equipment, such as grinders and kilns. By integrating a preventative maintenance strategy for these reducers, the cement plant can minimize unplanned downtime, optimize performance and extend the useful life of the equipment. This involves regular inspections, replacing worn parts before they fail, and properly lubricating components to ensure optimal operation. By investing in effective preventative maintenance, cement plants can save money in the long term by avoiding high costs associated with emergency repairs and production interruptions. The impact of a planetary gearbox in a cement plant therefore becomes significant.

Real-time predictive control assessment of low-water head hydropower station considering power generation and flood discharge

In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.

Enhanced performance of novel green synthesized CuS nanostructures decorated with Rh and Pd nanoparticles for energy generation and gas sensing applications

This article investigates the utilization of copper sulfide (CuS) based nanomaterials functionalized with rhodium (Rh) and palladium (Pd) nanoparticles for gas sensing and energy generation applications. CuS nanoparticles, known for their unique properties, were combined with Rh and Pd nanoparticles to enhance hydrogen (H2) gas sensing capabilities. Gas sensing experiments revealed that CuS/Rh/Pd nanocomposites exhibited the highest sensing response of 58.33% to H2, indicating the significant improvement in gas sensing properties due to the combined presence of Rh and Pd nanoparticles in CuS matrices. Moreover, temperature-dependent responses provided insights into optimal operating conditions for effective gas sensing with CuS-based nanocomposites. Comprehensive characterization studies including XPS, Raman, and morphology analysis confirmed the successful synthesis of CuS/Rh/Pd nanomaterials with high purity and desirable structural properties. Dye-sensitized solar cell (DSSC) performance studies demonstrated that CuS/Rh/Pd nanocomposites enhanced the efficiency by minimizing light over-absorption and improving photoanode stability. Overall, this study highlighted the potential of metal nanoparticle-decorated CuS nanocomposites for advanced gas sensing and energy generation applications, paving the way for the development of highly sensitive and efficient gas sensors and solar energy conversion technologies.

Collaborative optimization for energy hub and load aggregator considering the carbon intensity-driven and uncertainty-aware

To achieve optimization operation between the energy hub operator and the load aggregators, this paper proposes a collaborative model for the energy hub and load aggregator considering the carbon intensity-driven and uncertainty. Firstly, a time-coupled carbon emission flow model for energy storage is proposed to cope with the change in carbon intensity at the injection node. Meanwhile, a carbon intensity-based incentive price is developed for interruptible loads with diverse carbon intensity. Secondly, the Wasserstein metric-based distributionally robust optimization method is leveraged to height against the energy hub uncertainty, in which the infinite-dimensional distributionally robust optimization model is reformulated into a tractable linear programming. Thirdly, considering the multi-entity characteristics of energy hub and load aggregators, a collaborative optimization method based on the alternating direction method of multipliers algorithm is constructed. Finally, to meet the convergence requirements of the alternating direction method of multipliers algorithm, the iterative convexification algorithm is applied to transform the nonconvex bi-level energy hub-users model into an iterative convex model by avoiding binary variables and big-M parameters. Consequently, the energy hub and load aggregator can operate cooperatively and protect privacy in uncertain conditions. Simulation results verify the effectiveness of the proposed model and method. Simulation results demonstrate the effectiveness of the proposed model and method. Specifically, the iterative convexification algorithm achieved a profit of ¥1653.82 for the load aggregator, which is 24.4 % higher than the profit obtained by CONOPT (¥1329.73) and 20.3 % higher than that obtained by IPOPT (¥1374.69), thus providing substantial advantages over traditional nonlinear solvers.

Perovskite CsPbBr3 quantum dots enhanced In2O3 nanospheres for triethylamine detection at low temperature

In recent years, the vigorous industrial development of the country has brought about serious air pollution, and the development of low-temperature gas sensors with high response and short response/recovery time has become ideal for monitoring the industrial environment. This work demonstrates a composite gas sensor based on CsPbBr3 perovskite quantum dots (QDs) and In2O3 nanospheres for the detection of TEA gases at low temperatures. Compared with pure In2O3, the best responsive gas for the In2O3-CsPbBr3 QDs composite was triethylamine (TEA), with a response value of 52.92 at an optimal operating temperature of 60 °C. In addition, due to the sensitive optical properties of the CsPbBr3 QDs, UV irradiation of the In2O3-CsPbBr3 composites was performed, and the samples were found to have response values as high as 156.42. The In2O3-CsPbBr3 composite also has a short response recovery time (1/19 s), which is favorable for timely warning. The results of the gas-sensing experiments showed that the sensor has good sensitivity, reproducibility, and stability for triethylamine.

Response surface methodology and process optimization of spark plasma sintered parameters of Ti20Al20Cr5Nb5Ni19Cu12Co19 high entropy alloy

Study was carried out on the effect of milling and sintering process parameters on the relative density and microhardness property of a septenary non-equiatomic Ti20Al20Cr5Nb5Ni19Cu12Co19 high entropy alloy fabricated via spark plasma sintering (SPS) process at 100 °C/min constant heating rate, 5 min dwell time, and at a pressure of 50 MPa. A predictive model was developed through response surface methodology (RSM) using the SPS sintering temperature (ST) and milling time (MT) as the process variables. In order to reduce the number of experimental runs, the design of experiment (DOE) technique was used, to eventually avoid the trial-and-error process associated with conventional experimental procedures. The user-defined design (UDD) of the RSM was used to forecast the optimal operating parameters, and the outcome was validated by experiments. Findings indicate that MT and ST are important in achieving high densification, which leads to high densification and mechanical properties. Optimization model shows that, at MT of 7.20115 h and ST of 899.742 °C, desirable responses of 99.9 % relative density, percentage porosity of 0.0999994 % and a micro-hardness value of 835.21 HV can be attained.

Multilayer decoupling control of photovoltaic integrated heat pump based on expected value

Abstract In this paper, a multilayer decoupling control method for photovoltaic heat pumps based on expected values is proposed, which aims to achieve the optimal operation of the photovoltaic heat pump system and photovoltaic heat pump system. This method predicts the output energy of photovoltaic photovoltaic system and realizes multilayer decoupling control based on the load demand of the heat pump system. In order to adapt to the change in environment, the adaptive fuzzy PID compound control method is adopted to optimize the control strategy. Experimental results show that this method can effectively improve the system efficiency, reduce energy consumption, and demonstrate good adaptability and stability.