Optimal Operating Parameters Research Articles

Transesterification of mustard oil to biodiesel using activated carbon/Fe2(MoO4)3/K2CO3 as a novel heterogeneous nanocatalyst: Box-Behnken design-based optimization and Gaussian process regression

In this study, activated carbon/Fe2(MoO4)3/K2CO3 was synthesized as a novel heterogeneous catalyst and used for the first time for biodiesel production from mustard oil. The synthesized catalyst was appraised by Brunauer-Emmett-Teller (BET), Energy-dispersive X-ray analysis (EDX)/Map, Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and Raman analyses. The effect of operating parameters including methanol-to-mustard oil ratio (9:1–27:1), temperature (50–75 °C), catalyst concentration (1–6 wt%), and reaction time (1–6 h) on the biodiesel yield was investigated. The optimization of transesterification operating parameters through Response Surface Methodology (RSM) utilizing Box-Behnken design revealed a peak yield of 96.36 ± 0.1 %. This optimal yield was attained under the following conditions: a methanol-to-oil ratio of 21:1, a temperature of 65°C, a catalyst concentration of 4 wt%, and a reaction time of 4 hours. Furthermore, Gaussian Process Regression (GPR) was employed for the prediction of the biodiesel production yield and a satisfactory agreement was observed between the results of RSM and GPR. Reusability experiments indicated that the synthesized catalyst can be regenerated and reused in 4 transesterification reactions without experiencing a significant decrease in yield. The physical properties of biodiesel derived from mustard oil were examined, and it was observed that they were in agreement with ASTM D6751 and EN 14214 standards. According to the results of this study, it can be concluded that biodiesel production from mustard oil using the novel activated carbon/Fe2(MoO4)3/K2CO3 heterogeneous catalyst can be considered as a promising approach for generation of clean energy.

Prediction and optimization of performance parameters of solar collectors with flat and porous plates using ANN and RSM: Case study of Shahrekord, Iran

It is necessary to adopt measures to make optimal use of rich sources of solar energy, considering the limit resources of fossil fuels and increasing demand for energy. Using solar collectors is one of the methods to collect solar energy. The aim of this study was an investigation and prediction of the effect of the collector's slope angle and plate type on the collector performance using artificial neural network (ANN) approaches. Two different ANN models, multi-layer perceptron (MLP) and radial basis function (RBF), were applied to predict the performance parameters of solar collectors. The results indicated that the ANN-MLP and ANN-RBF models were acceptable, but the ANN-RBF model with structure of 2-16-5 had higher acceptability for prediction of output parameters at various input variables. The highest R and lowest root mean square error (RMSE) values in training set of ANN-RBF for air speed (AS) were obtained as 0.984 and 0.015, respectively. The results showed that increasing the slope angle from 0 to 60 caused to increase in the amount of received radiation by the collector, and so leading to an increase in the air temperature inside the collector. On the other hand, the maximum value of thermal power (TP) and collector efficiency (CE) for the porous plate were more than the simple plate by 28.2 % and 41.5 %, respectively.

Numerical Simulation Research on Combustion and Emission Characteristics of Diesel/Ammonia Dual-Fuel Low-Speed Marine Engine

Amid increasingly stringent global environmental regulations, marine engines are undergoing an essential transition from conventional fossil fuels to alternative fuels to meet escalating regulatory requirements. This study evaluates the effects of injection pressure, the timing of ammonia injection, and the pre-injection of ammonia on combustion and emissions, aiming to identify optimal operational parameters for low-speed marine engines. A three-dimensional model of a large-bore, low-speed marine engine in a high-pressure diffusion mode was developed based on computational fluid dynamics (CFD). Simulations were conducted under 25%, 50%, 75% and 100% loads with a high ammonia energy substitution rate of 95%. The results indicate that, compared to traditional pure diesel operation, adjusting the injection pressure and the ammonia injection timing, along with employing appropriate pre-injection strategies, significantly enhances in-cylinder pressure and temperature, improves thermal efficiency, and reduces specific fuel consumption. Additionally, the dual-fuel strategy using diesel and ammonia effectively reduces nitrogen oxide emissions by up to 37.5% and carbon dioxide emissions by 93.7%.

Generative adversarial network for optimization of operational parameters based on shield posture requirements

To mitigate the potential hazards of shield tunneling misalignment (STM) caused by tunneling posture deviation, a method for optimizing operational parameters tailored to tunneling posture adjustment is developed. This paper presents a generative adversarial network (GAN) framework that incorporate a conditional generative adversarial network (CGAN) and two distinct discriminators (WGAN and Path GAN) to enhance the performance of the multi operational parameter generator. Based on engineering data, comparative experiments are designed to investigate the impact of the feature extraction methods, discriminators, and training strategies on the generation performance. Research has shown that an optimal generator scheme, comprising independent convolutional neural networks (CNNs), a summation feature fusion strategy, and a shared decoder, achieves remarkable performance with an MAE of 0.009, RMSE of 0.012, and average error scope of 0.073. Applications of the model confirm its ability to provide optimization suggestions for shield tunneling posture adjustments in engineering scenarios.

Sustainable menaquinone-7 production through continuous fermentation in biofilm bioreactors.

Menaquinone-7 (MK-7), a vital vitamin with numerous health benefits, is synthesized and secreted extracellularly by the formation of biofilm, dominantly in Bacillus strains. Our team developed an innovative biofilm reactor utilizing Bacillus subtilis natto cells to foster biofilm growth on plastic composite supports to produce MK-7. Continuous fermentation in biofilm reactors offers a promising strategy for achieving sustainable and efficient production of Menaquinone-7 (MK-7). Unlike conventional batch fermentation, continuous biofilm reactors maintain a steady state of operation, which reduces resource consumption and waste generation, contributing to sustainability. By optimizing fermentation conditions, MK-7 production was significantly enhanced in this study, demonstrating the potential for sustainable industrial-scale production. To determine the optimal operational parameters, various dilution rates were tested. These rates were selected based on their potential to enhance nutrient supply and biofilm stability, thereby improving MK-7 production. By carefully considering the fermentation conditions and systematically varying the dilution rates, MK-7 production was significantly enhanced during continuous fermentation. The MK-7 productivity was found to increase from 0.12mg/L/h to 0.33mg/L/h with a dilution rate increment from 0.007 to 0.042h-1). This range was chosen to explore the impact of various nutrient supply rates on MK-7 production and to identify the optimal conditions for maximizing productivity. However, a further increase in the dilution rate to 0.084h-1 led to reduced productivity at approximately 0.16mg/L/h, likely due to insufficient retention time for effective biofilm formation. Consequently, a dilution rate of 0.042h-1 exhibited the highest productivity of 0.33mg/L/h, outperforming all investigated dilution rates and demonstrating the critical balance between nutrient supply and retention time in continuous fermentation. These findings validate the feasibility of operating continuous fermentation at a 0.084h-1 dilution rate, corresponding to a 48h retention time, to achieve the highest MK-7 productivity compared to conventional batch fermentation. The significant advancements achieved in enhancing Menaquinone-7 (MK-7) productivity through continuous fermentation at optimal dilution rates in the present work indicate promising prospects for even greater efficiency and sustainability in MK-7 production through future developments.

A novel zwitterionic magnetic nanocomposite developed for non-invasive speciation analysis of inorganic chromium

3-(2-Aminoethylamino)propyltriethoxysilane and carboxyethylsilanetriol sodium salt were grafted on silica-coated Fe3O4 nanoparticles via sol-gel process to prepare novel amine- and carboxyl-bifunctionalized magnetic nanocomposites (SMNPs-(NH2 + COOH)). After well characterized, this doubly functionalized material was used as magnetic solid-phase extraction (MSPE) adsorbent to separate and enrich inorganic chromium species followed by inductively coupled plasma-mass spectrometry detection. The optimization of MSPE operation parameters including pH was conducted. It is reasonably elucidated that the adsorption mechanisms of zwitterionic SMNPs-(NH2 + COOH) towards chromium species are electrostatic and/or coordination interactions. Cr(VI) and Cr(III) can be adsorbed around pH 3.0 and around 10.0 respectively with strong anti-interference ability not only from other co-existing ions but also from the two labile species each other, and eluted by dilute nitric acid solution. With a 15-fold enrichment factor, the limits of detection of Cr(VI) and Cr(III) were 0.008 and 0.009 μg L−1, respectively, profiting from the maximum adsorption capacities of 7.52 and 6.11 mg g−1. The just one magnetic extraction matrix based speciation scheme possesses excellent convenience and friendliness to Cr(VI) and Cr(III) without any oxidation or reduction prior to capture of these two species. This protocol has been successfully applied to the speciation analysis of inorganic chromium in real-world environmental water samples.

Optimization of Operational Parameters Using Artificial Neural Network and Support Vector Machine for Bio-oil Extracted from Rice Husk.

Bio-oil production from rice husk, an abundant agricultural residue, has gained significant attention as a sustainable and renewable energy source. The current research aims to employ artificial neural network (ANN) and support vector machine (SVM) modeling techniques for the optimization of operating parameters for bio-oil extracted from rice husk ash (RHA) through pyrolysis. ANN and SVM methods are employed to model and optimize the operational conditions, including temperature, heating rate, and feedstock particle size, to enhance the yield and quality of bio-oil. Additionally, ANN modeling is utilized to create a predictive model for bio-oil properties, allowing for the efficient optimization of pyrolysis conditions. This research provides valuable insights into the production and properties of bio-oil from RHA. By harnessing the capabilities of ANN and SVM, this research not only aids in understanding the intricate relationships between process variables and bio-oil properties but also provides a means to systematically enhance the production process. The predictive results obtained from the ANN were found to be good when compared with the SVM. Several models with different numbers of neurons have been trained with different transfer functions. R values for the training, validation, and test phases are around 1.0, i.e., 0.9981, 0.9976, and 0.9978, respectively. The overall R-value was 0.9960 for the proposed network. The findings were considered acceptable, as the overall R-value was close to 1.0. The optimized operational parameters contribute to the efficient conversion of RHA into bio-oil, thereby promoting the use of this sustainable resource for renewable energy production. This approach aligns with the growing emphasis on reducing the environmental impact of traditional fossil fuels and advancing the utilization of alternative and environmentally friendly energy sources.

Designing a Multi-Objective Optimization Framework for Global Supply Chains Using Genetic Algorithm and NSGA II

Supply chain optimization is crucial for firms to boost efficiency and competitiveness amidst increasing complexity and risks in global markets. This study provides light into the optimization of a global supply chain network using genetic algorithms, concentrated on avoiding disruptions and decreasing total supply chain costs. The main objectives comprise decreasing total supply chain costs, managing disruptions in product quantity and quality, and optimizing operating parameters and features. The study employs genetic algorithms for optimization, explaining the approach and providing final interpretations to boost supply chain efficiency and resilience against disturbances.

Hydrogen fueling station: review of the technological state of hydrogen fuel usage

RELEVANCE. In the modern world, on the verge of global climate change, the search and implementation of alternative energy sources become particularly significant. Hydrogen energy is one of the most promising directions, offering a revolutionary approach to the decarbonization of various industrial sectors. The development of technologies related to the production, storage, and use of hydrogen opens new horizons for creating a sustainable and environmentally friendly energy infrastructure.OBJECTIVE. To review the technological state of hydrogen refueling stations (HRS), analyze the latest global trends and developments in this area, identify factors contributing to the efficiency of HRS components, and present thermodynamic principles of hydrogen fuel use. To outline the main problems associated with the need for widespread implementation of hydrogen infrastructure and to identify potential directions for their resolution. To develop suggestions for creating a modular layout of a container-type hydrogen station, which will allow a flexible approach to organizing hydrogen infrastructure with the possibility of rapid scaling and adaptation under various operating conditions.METHODS. Based on the use of literature data. The method of prototyping an autonomous hydrogen refueling station was used, and mathematical calculations of thermodynamic processes occurring in the components of HRS were conducted.RESULTS. Studies in the field of technological state of stations have been examined and systematized, and development trends have been identified. The main components involved in the operation of a hydrogen station are described. Thermodynamic processes of hydrogen fuel use that contribute to a significant reduction in energy consumption of hydrogen stations have been investigated.CONCLUSION. The hydrogen station combines efficient conversion of hydrogen into electricity, minimization of emissions, energy independence, and flexibility in energy storage. Determining the optimal operating parameters of HRS equipment based on the thermodynamics of processes, considering the specifics of temperature regimes of Russian regions, is important for reducing costs and increasing the energy efficiency of hydrogen fuel systems. The proposed structure of the container-type HRS is an optimal platform for subsequent modernizations and innovations in the field of hydrogen technologies.

Effect of Operating Conditions on the Capacity of Vanadium Redox Flow Batteries

Vanadium redox flow batteries (VRFBs) present a viable solution to address the intermittent power output challenge associated with wind and solar energy generation. However, their development is impeded by their low energy density and high cost. To achieve the objective of cost reduction, it is crucial to optimize operating conditions, minimize capacity loss, and enhance battery performance. Through meticulous experimental analysis, this study thoroughly examines the impact of membrane thickness, current density, flow rate, and self-discharge on battery capacity. The experimental findings reveal that an increase in membrane thickness results in elevated resistance to proton transport, thereby weakening electrochemical reactions. Moreover, surpassing critical values for current density and flow rate also leads to a decrease in capacity. Prolonged shelving induces severe self-discharge reactions that accelerate deterioration of capacity fade. This research suggests that obtaining optimal operational parameters can effectively mitigate battery capacity fade.

Modeling and simulating the thermal management system of a hydrogen fuel cell

Abstract Hydrogen energy is an important means of achieving decarbonization goals. Hydrogen fuel cells have broad application prospects due to their high-power output and wide temperature range. To improve the heat dissipation capacity of the fuel cell thermal management system, this paper establishes a simulation model of a 60kw fuel cell. The performance of the fuel cell engine thermal management system model under optimal operating parameters is studied by taking the temperature of the inlet and outlet water of the stack as the observation value. This is significant in improving the thermal environment of the fuel cell engine and ensuring its safe and efficient operation.

Power feasibility of single-staged full-scale PRO systems with hypersaline draw solutions

Pressure retarded osmosis (PRO) is a process that allow to generate energy from osmotic gradient. This process uses selective membranes in order to produce electrical energy through a hydraulic turbine. PRO can be used as a renewable energy technology where water resources are inexhaustible. PRO has the advantage of knowing when and how much energy will be produced. Unfortunately at the moment there are certain limiting factors concerning membrane and module characteristics that have prevented PRO to be fully exploited at full-scale. This study aims to assess the impact of hypersaline draw solutions (60–180 g L−1), membrane characteristics such as structural parameter, module membrane surface and permeability coefficients on the net energy generated by single-staged full-scale PRO system with up to 8 spiral wound membrane modules (SWMMs) in series in a pressure vessel. To carry out this study, characteristics of existing PRO membranes at lab-scale were scaled up to 8 inches SWMM. The results showed the change in the optimal operating parameters with the change of membrane characteristics and draw solution concentration. This study concluded that single-staged full-scale PRO process would be viable from an energy point of view if membranes were manufactured on an industrial scale and with the characteristics of existing membranes on a laboratory scale.

Numerical Modeling of Heat Transfer and Flow Field in a Novel Calcinator

Abstract This study focused on investigating the heat transfer and flow dynamics of a catalyst granule within a pilot calciner, employing both numerical modeling and computational fluid dynamics. The research comprised two primary components: (1) Simulation of the gas flow within the pilot calciner using the Eulerian–Eulerian approach, treating gases and catalyst particles as distinct phases – gas and granular. The model, encapsulating both heat transfer and flow processes, was developed in Fluent software version 16.0. Its accuracy was confirmed against empirical data from a pilot-scale calciner unit. (2) Subsequent to validation, the model was utilized to examine the distribution characteristics within the flow field, including the temperature profiles of gas and particles, the vector velocity field of the gas across different phases, and the overall heat transfer coefficient. This investigation aims to enhance the understanding of the complex heat transfer and flow dynamics in calciners, facilitating the optimization of operational parameters, performance, and structure of pilot-scale equipment. Furthermore, it provides foundational data pertinent to the future exploration of real-world industrial applications.

Computational boundary improvement and performance optimization of the flue gas waste heat cascade utilization system of the ultra-supercritical 1000 MW generator set

Abstract There is no uniform performance test rule and standard calculation method for efficiency improvement of a system that utilizes waste heat from flue gas and it is mainly carried out through turbine parameters, without considering boiler system performance. Given the above problem, in this paper, the steam turbine, boiler and waste heat from the flue gas step-up utilization system are calculated by establishing a thermal model of ultra-supercritical secondary reheating 1000 MW unit. At the same time, unit design data and field performance test results are used for verification. Analysis comparing simulation outcomes with experimental data revealed that the discrepancies in the heat consumption rate per unit, the efficiency of the boiler, and the rate of coal consumption for power generation peaked at a marginal variance of 0.2%. The optimum operating parameters of the system for low-grade flue gas recovery in a cascading manner are obtained through variable operating condition calculations. The optimum value of high-pressure bypass feed-water flow rate is 52 kg/s at 970 MW operating condition. At the same time, the difference between high-pressure bypass effluent and high plus effluent temperatures should be controlled to be close to 0°C, and the optimal value of low-pressure bypass condensate flow rate is 53 kg/s. As the flow of flue gas in the bypass increases, the efficiency of heat exchange in the bypass economizer also increases, resulting in greater absorption of heat in the utilization apparatus for flue gas waste heat, leading to a more effective energy-saving outcome. However, it is necessary to regulate the temperature of both the primary and secondary air to levels that exceed the minimum requirements for the safe functioning of the coal mill.

A method for identifying the optimal operating parameters of an internal combustion engine diagnostic device

A method for identifying the optimal operating parameters of an internal combustion engine diagnostic device

Innovative Technology for Managing Biofuel Production from Timber Industry Waste

The relevance of this study is determined by the growing worldwide interest in renewable energy sources against the backdrop of depleting fossil fuel reserves. This study aims to develop an innovative technology for managing biofuel production from wood waste, including a set of interrelated economic and mathematical models focused on maximizing the fuel and energy efficiency of biofuels depending on the location of waste generation, feedstock moisture content, and distance to the biofuel production site. This technology should also combine the main directions of international research in the field of environmental responsibility of countries in terms of carbon dioxide (CO2) emissions and the Paris Climate Agreement. The methodological basis of the research comprises the authors’ innovative technology based on a set of interconnected economic and mathematical models and managerial decision-making systems, methods for nonlinear programming, system analysis, an information approach to the analysis of systems, accepted technological processes, norms, and standards established in the international practice of the timber industry. This innovative technology was implemented in practice using the capabilities of the MathCad and MS Excel software products. The article determines the optimal operating parameters of timber industry enterprises at which the specific thermal energy of the produced biofuel exceeds by at least 15% the thermal energy spent on processing this biofuel as an energy carrier. Wood waste biofuel production is profitable if the distance for feedstock transportation to the production site does not exceed 80 km and the relative humidity of the raw materials does not exceed 60%. Doi: 10.28991/ESJ-2024-08-03-03 Full Text: PDF

Performance analyses on the air cooling battery thermal management based on artificial neural networks

Air cooling thermal management technology is a lightweight and cost-effective approach for managing heat in power battery packs for electric ships. However, it suffers from significant drawbacks such as poor temperature control and excessive noise generation. To address these challenges, this study proposes a method that combines fluid dynamics and artificial neural networks (ANNs) to optimize the thermal management and aeroacoustic performance of the air cooling battery thermal management system (BTMS) for marine power batteries. Initially, a thermal-flow coupled model and an acoustic model for the BTMS were developed to investigate the impact of system structural parameters (battery spacing d, and main channel inclination angle θ) and operating parameter (system inlet velocity v) on the thermal management performance indicators (maximum temperature Tmax, and maximum temperature difference ΔTmax) as well as the aeroacoustic performance indicator (overall sound pressure level, OSPL). Subsequently, a relationship between the system structural and operating parameters and performance indicators was established using the 50-layer Residual Network (ResNet-50) model, enabling accurate and rapid prediction of the system thermal management and aeroacoustic performance. Furthermore, by combining ResNet-50 with the evaluation method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), this study successfully obtained the optimal system structure and operating parameters. The research indicates that keeping the remaining parameters constant, increasing the battery spacing results in higher maximum temperature and maximum temperature difference, while decreasing the overall system sound pressure level. Conversely, increasing the main channel inclination angle results in lower maximum temperature and maximum temperature difference, but higher overall system sound pressure level. In addition, increasing the inlet velocity will result in higher maximum temperature and maximum temperature difference, as well as higher overall system sound pressure level. The optimal case were found to be a main channel inclination angle θ = 3°, battery spacing d = 2 mm, and inlet velocity v = 10 m⋅s−1. Compared to the base case, the optimal case shows a maximum temperature reduction of 10.63 K, a maximum temperature difference reduction of 9.41 K, and an overall sound pressure level reduction of 4.6 dB. The prediction errors for these values are 0.08 %, 2.64 %, and 1.36 % respectively. This research demonstrates an effective and rapid approach based on fluid dynamics and ANNs in the design and optimization of BTMS.

Study on the Pumping Performance and Structure Parameters Optimization of High-Speed Small Compound Molecular Pump.

A molecular pump is the core component of vacuum systems in portable mass spectrometers and other analytical instruments. The forms of the existing molecular pumps mainly are the combinations of vertical bleed and compression channel, which have the shortcomings of heavy mass and large volume, which seriously restricts the application and development of portable mass spectrometers. Aiming at the problems of low strength and insufficient pumping performance under the miniaturization constraints (mass of 1.8 kg; exhaust diameter of 25 mm) of molecular pumps, a compound pump consisting of a horizontal bleed channel and multi-stage spiral compression channel is proposed. The pumping principle of the compound molecular pump is analyzed to obtain its preliminary structural size parameters. The test particle Monte Carlo method is presented for establishing an aerodynamic model for a high-speed small compound molecular pump, which can be used to investigate the pumping performance of bleed blades and compression channels in a thin air environment. On the basis of the aerodynamic model, the NNIA multi-objective optimization algorithm is presented to optimize the structural parameters of the compound molecular pump. After structural parameter optimization, the maximum flow rate and compression ratio of the compound molecular pump are increased by 13.6% and 41.6%, respectively. The experimental results of the pumping performance show that the predicted data of the aerodynamic model are in good agreement with the experimental data, with an error of 12-27%. Namely, the established aerodynamic model has high accuracy and the optimized structural parameters of the compound molecular pump can provide basic conditions for the large-scale application and promotion of portable mass spectrometers.

Asymmetric CapMix Device Exploiting Functionalized Graphene Oxide and SPEEK Coatings for Improved Energy Harvesting From Salinity Gradients

AbstractThis work investigated the possibility of using functionalized graphene oxide and sulfonated poly(ether ether ketone) coatings on activated carbons to obtain an asymmetric device suitable for Capacitive Mixing (CapMix) application. Herein the synthesis and fabrication procedures of both materials and electrodes are reported, together with a comprehensive list of electrochemical characterization techniques. The results from the electrochemical characterization are used to design the optimal operational parameters for CapMix experiments involving the materials being studied. A homemade cell is designed and fabricated for CapMix application by polymer micromachining. Electrochemical tests are conducted to optimize the process parameters and the results of this optimization are reported, proving that both the materials and the process parameters play a key role in CapMix. The final device can generate a net power output of 12 mW m−2 by mixing simulated seawater and freshwater. This result paved the way for further application of graphene oxide in the field of renewable energy.

A two-phase nonlinear optimization method for routing and sizing district heating systems

This paper presents a method to find the optimal topology, pipe sizing, and operational parameters of a district heating system under consideration of one design point. The current high costs of district heating systems set limits regarding the minimum heat demand density required for economic network expansions. Optimized routing with ideal pipe sizing and optimal operating parameters offers a potential for cost reduction. Therefore, this paper introduces a new two-phase method for district heating network expansion planning. This method consists of consecutive optimizations, starting with a mixed-integer linear programming followed by a nonlinear optimization. During the mixed-integer linear programming, the district heating system is optimized with continuous diameters, and the nonlinear pressure and temperature dependencies must be linearized. The resulting topology and the continuous diameters are afterward handed over to a nonlinear sparse sequential quadratic programming. The continuous diameters are discretized using a numerical continuation strategy that gradually forces the continuous diameter variables into discrete diameter choices. As a proof of concept, the district heating system for a small town with 400 consumers is optimized and analyzed. The two-phase optimization is performed in 251.68sec, and in most cases, discrete or near discrete diameters are achieved in a nonlinear continuous optimization.