Optimal Combination Of Operating Parameters Research Articles

Low-carbon scheduling of electricity consumption in wastewater treatment plant by using photovoltaic system

Sewage treatment as a high energy consumption industry, its electricity consumption accounts for 3 % of the total electricity consumption of society. That means significant greenhouse gas emissions. In the context of China's goal of “reaching carbon peak by 2030 and achieving carbon neutrality by 2060”, reducing the energy consumption of wastewater treatment systems has emerged as an important issue in recent years. In this paper, the GPS-X simulation software was employed to conduct a simulation study of a modified Anoxic-Aerobic-Oxic wastewater treatment plant (WWTP) in Wuhan, and the response surface methodology (RSM) was utilized to ascertain the interactive effects of DO, IRF, ERR, and SD on the effluent quality, thereby identifying the operational parameters that minimize energy consumption while maintaining satisfactory effluent quality. Additionally, the PVsyst software was employed to design the solar power generation system of the WWTP and analyze its power generation potential. On this basis, through the coupling of photovoltaic power, electricity load, time-of-use pricing, and the water quality simulation model, and taking the WWTP data in September as a case study, the electricity usage strategies under various illumination conditions were formulated. The aim is to maximize the use of photovoltaic power to reduce the cost and carbon emissions of the WWTP. The results show that the optimal combination of operational parameters, including an external reflux ratio of 0.3, the internal recycle flow of 50,000 m3/d, and the sludge discharge of 448 m3/d, resulted in a reduction in power of 208.5 kW, and after the combination optimization of operational parameters and electricity utilization, the operation cost of the WWTP in September was reduced by 40 % ∼ 60 %, and the carbon emission attributable to electricity was reduced by 30 % ∼ 50 %.

Performance Assessment and Optimization of the Ultra-High Speed Air Compressor in Hydrogen Fuel Cell Vehicles

Air compressors in hydrogen fuel cell vehicles play a crucial role in ensuring the stability of the cathode air system. However, they currently face challenges related to low efficiency and poor stability. To address these issues, the experimental setup for the pneumatic performance of air compressors is established. The effects of operational parameters on energy consumption, efficiency, and mass flow rate of the air compressor are revealed based on a Morris global sensitivity analysis. Considering a higher flow rate, larger efficiency, and lower energy consumption simultaneously, the optimal operating combination of the air compressor is determined based on grey relational multi-objective optimization. The optimal combination of operational parameters consisted of a speed of 80,000 rpm, a pressure ratio of 1.8, and an inlet temperature of 18.3 °C. Compared to the average values, the isentropic efficiency achieved a 48.23% increase, and the mass flow rate rose by 78.88% under the optimal operational combination. These findings hold significant value in guiding the efficient and stable operation of air compressors. The comprehensive methodology employed in this study is applicable further to investigate air compressors for hydrogen fuel cell vehicles.

Multi‐objective Optimization of Geothermal Extraction from the Enhanced Geothermal System in Qiabuqia Geothermal Field, Gonghe Basin

AbstractA geothermal demonstration exploitation area will be established in the Enhanced Geothermal System of the Qiabuqia field, Gonghe Basin, Qinghai–Xizang Plateau in China. Selection of operational parameters for geothermal field extraction is thus of great significance to realize the best production performance. A novel integrated method of finite element and multi‐objective optimization has been employed to obtain the optimal scheme for thermal extraction from the Gonghe Basin. A thermal‐hydraulic‐mechanical coupling model (THM) is established to analyze the thermal performance. From this it has been found that there exists a contraction among different heat extraction indexes. Parametric study indicates that injection mass rate (Qin) is the most sensitive parameter to the heat extraction, followed by well spacing (WS) and injection temperature (Tin). The least sensitive parameter is production pressure (pout). The optimal combination of operational parameters acquired is such that (Tin, pout, Qin, WS) equals (72.72°C, 30.56 MPa, 18.32 kg/s, 327.82 m). Results indicate that the maximum electrical power is 1.41 MW for the optimal case over 20 years. The thermal break has been relieved and the pressure difference reduced by 8 MPa compared with the base case. The optimal case would extract 50% more energy than that of a previous case and the outcome will provide a remarkable reference for the construction of Gonghe project.

An integrated multi-objective optimization method to improve the performance of multilateral-well geothermal system

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.

Numerical simulation and field tests of minimum-tillage planter with straw smashing and strip laying based on EDEM software

The advanced and comprehensive utilization and entire burning prohibition of fully covered crop straw in farmlands have become significant in modern agriculture. Considering the high straw-returning yields, complicated operating procedures, and tight crop-stubble rotation, as well as high power consumption, poor smoothness shun, and low multiple-seed index obtained using traditional compound operation machines for rice and wheat rotation regions in the middle and lower reaches of the Yangtze River, this study extends the concept of the new ‘clean-area planting’ pattern. A minimum-tillage planter with straw smashing and strip laying was developed for full straw mulching fields. In a single pass, the planter could complete multiple operation processes, including straw-stubble smashing, seed-belt cleaning, inter-row stacking, seed-bed treatment, fertilization sowing, and soil covering and suppression. A mechanical model for the straw particle was established using the discrete element method (DEM) and EDEM software, and DEM virtual simulation tests and numerical analysis were conducted. A quadratic rotating orthogonal center combination test was implemented by setting the smashing spindle speed n′(A), machine ground speed v(B), and rotation tilling spindle speed n(C) as the influence factors, with the straw mulch uniformity ε1 and the coefficient of straw ridge disturbance ε2 as the evaluation indices. Response surface optimization analysis was performed to obtain the optimal combination of operational parameters affecting the planter working performance. The results indicated that the most significant factors affecting the straw mulch uniformity, ε1, and coefficient of straw ridge disturbance, ε2, were: smashing spindle speed, n′, > machine ground speed, v, > rotation tilling spindle speed, n, and rotation tilling spindle speed, n, > machine ground speed, v, > smashing spindle speed, n′. The optimal combination of these operational parameters was n′ = 2042.35 r/min, v = 0.99 m/s, and n = 440.93 r/min, providing a maximum ε1 = 94.30% and minimum ε2 = 9.67%. Field verification test results indicated that the optimized minimum-tillage planter achieved mean values of ε1 = 86.25% and ε2 = 9.75%, with average relative errors of 8.51% and 10.27% compared to the simulation tests, respectively. The accuracy and effectiveness of the DEM simulation model were verified. Moreover, the stipulated industry standards and operation requirements of no-tillage planter machinery with a straw-returning field can be satisfied, and technical support can be provided for follow-up studies of similar conservation tillage equipment.

Selecting Optimal Combination of Operating Parameters of VCR Diesel Engine Adopting AHP

Objective: To select the optimal combination of Variable compression ratio (VCR) diesel engine operating parameters using Sesbania aculeate biodiesel. Method/Statistical Analysis: The Analytical Hierarchy Process(AHP)approach is a mathematical approach used to find the optimal combination of VCR diesel engine operating parameters and the best blend of Sesbania aculeata biodiesel. Findings: It provides the alternatives and also the visualization of various criteria and their interrelations. The alternatives are ranked by measuring the criteria and their relative importance. The permanent concept helps for the better appreciation of criteria and considers the problem selection as it contains possible criteria components and their relative importance. It was found that the optimal combination of 100% load, 17.5 compression ratio and SAOME20 blend is the optimal combination of operating parameters of the VCR diesel engine. Novelty/Improvement: The best combination of operating parameters can be found systematically and logically using AHP. Keywords: Matrix, Permanent Index, Sesbania Aculeata Biodiesel, VCR Engine, AHP

Optimization of microalgae-sourced lipids production for biodiesel in a top-lit gas-lift bioreactor using response surface methodology

Microalgae bioreactors that capture industrial carbon dioxide (CO2) emissions to produce lipids for biodiesel are of significant interest. Sun-lit open raceways are generally considered the most economic method for mass cultivation, but the large physical footprint of these shallow systems can limit both industrial site availability and CO2 transfer into the culture medium. To address these issues, a deep top-lit only, gas-lift bioreactor to culture microalgae and capture CO2 was designed and investigated. The results show a three times increase in areal biomass and lipid productivities when compared to traditional raceways used in large-scale microalgae production. Operational factors exerting significant effects on areal biomass and lipid productivities were identified through Plackett-Burman experimental design as gas flow rate, feed gas CO2 content, and dispersion height. By employing response surface methodology, models to predict areal biomass and lipid productivities were derived. The resulting desirability function was then applied to obtain the optimal combination of operational parameters that maximize lipid production per unit area occupied by the bioreactor, whilst keeping biomass production low to reduce downstream processing costs. The optimum operational parameters that fulfill the requirements of the optimization function resulted in areal biomass productivity of 32.1 gdwm−2d−1 and areal lipid production of 198.4 gLipidm−2.

Design and Experimental Study of a Mower Conditioner Drive System

According to the forage of mountain region harvesting requirements, a transmission of montanic mower conditioner was designed. The drive system was combined with automatic tension sprocket and double chain wheel of upper and lower crushers, firstly, to realize the automatic regulation and relative motion of two flattening roller, secondly, the power was passed to reel device and achieved forage feeding function; the off-center slider crank mechanism drove the horizontal header to realize the grass cutting function. Through the theoretical analysis and experimental study of the system working performance, applied comprehensive scoring method to obtain the optimal combination of working parameters: the cutting ration was3, the press roller circumferential speed was 4.5m/s, the reel speed ratio was 3. Measured with the optimal combination of operational parameters, the results showed that: the working stubble was 40mm, the recutting rate was 0.8%, the flattening ratio was 42.8% and the crushed grass loss rate was 2.3%, which could meet the demand of forage harvest operation.

Simultaneous removal of arsenic and fluoride from groundwater by coagulation-adsorption with polyaluminum chloride

The ArCIS-UNR® arsenic and fluoride removal system which consists of a coagulation-adsorption process with initial pH adjustment and two filtration stages (up-flow gravel roughing filtration and rapid filtration), has been already described in previous studies. This process has been successfully implemented in full-scale plants in Argentina, with fluoride and total arsenic concentrations in raw water up to 2 mg/L and 200 μg/L, respectively. The aim of the present study is to optimize fluoride removal when it is >2.0 mg/L. The modifications proposed were to decrease the pH level to 6.4–6.6 at the roughing filter inlet and increase the pH level to 7.2–7.6 at the rapid filter inlet to decrease residual aluminum concentration. Laboratory and pilot scale studies were performed using natural water with fluoride concentrations ranging from 2.4 to 3.2 mg/L and arsenic concentrations from 60 to 90 μg/L. An optimal combination of operational parameters and the coagulant dose (polyaluminum chloride, PACl) was found, to achieve final concentrations of fluoride, arsenic and aluminum in treated water lower than those set by the regulations in force. Filtration run time was 10 to 12 hours; research studies are necessary to extend this period.

Microalgae biomass and lipid production potential, and mechanical stress compliance

Microalgae bioreactors that capture industrial carbon dioxide (CO2) emissions to produce lipids for biodiesel are of significant interest. Sun-lit open raceways are generally considered the most economic method for mass cultivation, but the large physical footprint of these shallow systems can limit both industrial site availability and CO2 transfer into the culture medium. To address these issues, a deep top-lit only, gas-lift bioreactor to culture microalgae and capture CO2 was designed and investigated. The results show a three times increase in areal biomass and lipid productivities when compared to traditional raceways used in large-scale microalgae production. Operational factors exerting significant effects on areal biomass and lipid productivities were identified through Plackett-Burman experimental design as gas flow rate, feed gas CO2 content, and dispersion height. By employing response surface methodology, models to predict areal biomass and lipid productivities were derived. The resulting desirability function was then applied to obtain the optimal combination of operational parameters that maximize lipid production per unit area occupied by the bioreactor, whilst keeping biomass production low to reduce downstream processing costs. The optimum operational parameters that fulfill the requirements of the optimization function resulted in areal biomass productivity of 32.1 gdwm−2d−1 and areal lipid production of 198.4 gLipidm−2.