Optimization Of Operating Conditions Research Articles

Simultaneous extraction and deglycosylation for flavonoid analysis in Ginkgo biloba products using a two-phase deep eutectic solvent system

Total flavonoid content is one of the most important quality indicators of Ginkgo biloba leaves and its products, but the complex pretreatment procedures and the excessive use of organic solvents complicate the detection process. In response, an innovative and efficient two-phase deep eutectic solvent (DES) system—composed of an acidic hydrophilic DES and a hydrophobic DES—was developed to quantify flavonoids for quality control of G. biloba products. High-performance liquid chromatography (HPLC) detection conditions were optimized, followed by systematic screening and design of the two-phase DES system through adjustments to polarity, hydrophilicity, and acidity. Optimal pretreatment conditions were established through operating condition optimization. This approach enabled simultaneous extraction, deglycosylation, and enrichment of all flavonoids from G. biloba samples into the hydrophobic phase in a single step. Method validation showed strong linearity with a correlation coefficient (R2 > 0.9995). The detection (LOD) and quantification (LOQ) limits were between 0.35 and 0.37 mg/kg, and 0.92 and 0.96 mg/kg, respectively. The method also exhibited high sensitivity and accuracy, with a recovery rate of 96.91–100.39%. A greenness assessment with the ComplexGAPI tool confirmed this method’s eco-friendly advantages over conventional techniques, aligning well with green chemistry and economic principles. Ultimately, this technique effectively measured flavonoid levels in different G. biloba products. The method developed in this work not only enhances the efficiency of simultaneous extraction and deglycosylation by modifying the polarity, hydrophilicity, and acidity of the two-phase DES system, but also offers valuable insights for the advancement of environmentally friendly analysis technology for flavonoids.

Optimization of operational conditions in adsorption processes using activated carbons for CO2 reuse in greenhouses

Optimization of operational conditions in adsorption processes using activated carbons for CO2 reuse in greenhouses

Application of Response Surface Methodology for the Optimization of Operating Conditions of a Self‐Pulsing Discharge (SPD) Plasma Reactor for the Degradation of Perfluorooctanoic Acid (PFOA) in Water

ABSTRACTThis study explores atmospheric plasma as a novel approach for the degradation of persistent per‐ and polyfluoroalkyl substances (PFAS) in contaminated waters. Using response surface methodology and Box–Behnken design, the performance of the self‐pulsing discharge (SPD) reactor was optimized by adjusting the following independent factors: input power, plasma area‐to‐liquid volume ratio, and argon bubbling time. Optimization was assessed using four specific indicators: kPFOA and G50, for the process velocity and energy efficiency, respectively; kPFOA/kPFHpA and ΣPFAS/C0, both for the presence of PFAS in the treated water for the process products. Under the optimized operating conditions, residual PFAS summed up to only 2.4% of the carbon initially present as PFOA, and a remarkable G50 value of (523 ± 10) mg/kWh was obtained.

Optimization of Operating Conditions for Battery Thermal Management System

With the rapid increase in the number of vehicles worldwide, we are currently experiencing a scarcity of nonrenewable energy, such as fuel, and the resulting environmental risks associated with vehicle utilization [...]

Machine learning-driven prediction of phosphorus adsorption capacity of biochar: Insights for adsorbent design and process optimization

Phosphorus (P) pollution in aquatic environments poses significant environmental challenges, necessitating the development of effective remediation strategies, and biochar has emerged as a promising adsorbent for P removal at the cost of extensive research resources worldwide. In this study, a machine learning approach was proposed to simulate and predict the performance of biochar in removing P from water. A dataset consisting of 190 types of biochar was compiled from literature, encompassing various variables including biochar characteristics, water quality parameters, and operating conditions. Subsequently, the random forest and CatBoost algorithms were fine-tuned to establish a predictive model for P adsorption capacity. The results demonstrated that the optimized CatBoost model exhibited high prediction accuracy with an R2 value of 0.9573, and biochar dosage, initial P concentration in water, and C content in biochar were identified as the predominant factors. Furthermore, partial dependence analysis was employed to examine the impact of individual variables and interactions between two features, providing valuable insights for adsorbent design and operating condition optimization. This work presented a comprehensive framework for applying a machine learning approach to address environmental issues and provided a valuable tool for advancing the design and implementation of biochar-based water treatment systems.

An approach to Molten Salt Reactor operation and control and its application to the ARAMIS actinide burner

Molten salt reactors (MSRs) are Generation IV nuclear reactor concepts gaining significant research interest worldwide. The present work proposes a methodology for the definition and optimization of the MSR operating conditions and control strategy. The proposed methodology employs a novel 0D steady-state model, the multi-physics system-scale MOSAICS (MOlten SAlt Incompressible Calculation System) model and a Global Sensitivity Analysis (GSA) method based on Hilbert-Schmidt Independence Criterion indices. The methodology was then applied to the ARAMIS (Advanced Reactor for Actinides Management in Salt) fast-spectrum chloride-salt burner reactor. Two alternative control strategies are proposed in order to achieve target margins to the salt freezing and structure material limit temperatures during normal operation, plus a 20 % power variation per minute load-following objective. The performance of the control strategies was assessed through comparison with the natural behavior during a load-increasing transient, as well as during Unprotected Transient Over-Power (UTOP) and Station Blackout (SBO) accidents. Controlled load variation transients are observed to reduce overall temperature variation rates throughout the salt circuits, while the inclusion of a variable fuel flow from the reactor commands can further limit these fluctuations. However, the selected operating conditions exhibited insufficient margins to freezing or the materials limit temperature under unprotected accident conditions. GSA enabled the derivation of correlations to characterize the dynamic response of MSRs to the accidents. Based on the observed reactor response to the various transients, potential modifications to the ARAMIS design and control strategies are proposed.

Discovery of an End-to-End Pattern for Contaminant-Oriented Advanced Oxidation Processes Catalyzed by Biochar with Explainable Machine Learning.

The utilization of biochar-catalyzed peroxymonosulfate in advanced oxidation processes (BC-PMS AOPs) is widely acknowledged as an effective and economical method for mitigating emerging contaminants (ECs). Especially, state-of-the-art machine learning (ML) technology has been employed to accurately predict the reaction rate constants of EC degradation in BC-PMS AOPs, primarily focusing on three aspects: performance prediction, operating condition optimization, and mechanism interpretation. However, its real application in specific degradation optimization targeting different ECs is seldom considered, hindering the realization of contaminant-oriented BC-PMS AOPs. Herein, we propose a hierarchical ML pipeline to achieve an end-to-end (E2E) pattern for addressing this issue. First, the overall XGB model, trained with the comprehensive data set, can perform well in predicting the reaction constants of EC degradation in BC-PMS AOPs, additionally providing the basis for further analysis of various ECs. Then, the submodels trained with different EC clusters can offer specific strategies for the selection of the optimum option for BC-PMS AOPs of specific ECs with different HOMO-LUMO gaps, thus forming an E2E operating pattern for BC-PMS AOPs. This study not only increases our understanding of contaminant-oriented optimization of AOPs but also successfully bridges the gap between ML model development and its environmental application.

Feedback system for reactor process analysis

The article discusses an approach to analyzing processes in industrial reactors using advanced systems with feedback. The authors describe the system preparation and image reconstruction algorithms of ultrasonic tomographs (USTs) that have applications in industry. The research presented in this paper focuses on developing effective strategies for controlling and monitoring chemical reaction processes in reactors. Using advanced data processing techniques, the authors propose systems with feedback that enable accurate analysis and optimization of reactor operating conditions. A key aspect is ultrasonic tomographic imaging, which provides precise data on the state of the process in the reactor. In summary, the paper presents a state-of-the-art approach to analyzing chemical reaction processes in industrial reactors using advanced feedback systems and ultrasonic imaging technologies. The proposed solutions are essential for improving efficiency and process control in the chemical industry.

Optimization of Lignocellulosic Enzyme production by Microbial Strains: Study of Kinetics and Environmental Influences

In the context of valorizing lignocellulosic biomass for biofuel production a mixture of forest residues (FW) and aromatic-medicinal plant wastes (AMPW) was considered in this study to assess its ability to support the production of cellulolytic enzymes by Aspergillus niger, Trichoderma longibrachiatum and Zymomonas mobilis. The residue mixture promoted growth and cellulase production by the microbial strains after 6 days of incubation. Enzymatic activity was determined using filter paper activity (FPase) and endoglucanase carboxymethylcellulase (CMase) assays. Optimization of enzymatic activity was investigated at pH levels (2, 4, 6, 8), various temperatures (22°C, 30°C, 37°C, 45°C) and different inoculum rates (1.5 × 107, 2 × 107 and 2.5 × 107 spores per gram of substrate). Maximum enzymatic activity was observed in Aspergillus niger with FPase and CMCase yields of 23.12 ± 0.1 and 29.78 ± 0.15 U/gds respectively. The highest enzymatic activity was recorded at temperatures of 30°C and 37°C, pH levels of 4 and 6 and with an inoculum rate of 2.5 × 107. The selection of the strain and the optimization of operational conditions are crucial for efficient enzymatic production in the context of biorefinery.

Advancing nanoarchitectures of 2D WO3/MXene photoanode for enhanced photoelectrocatalytic oxidation of phenol and arsenic in synthetic wastewater

The photoelectrocatalytic advanced oxidation process (PEAOP) necessitates high-performing and stable photoanodes for the effective oxidation of complex pollutants in industrial wastewater. This study presents the construction of 2D WO3/MXene heteronanostructures for the development of efficient and stable photoanode. The WO3/MXene heterostructure features well-ordered WO3 photoactive sites anchored on micron-sized MXene sheets, providing an increased visible light active catalytic surface area and enhanced electrocatalytic activities for pollutant oxidation. Phenol, a highly toxic compound, was completely oxidized at an applied potential of 0.8 V vs. RHE under visible light irradiation. Systematic optimization of operational conditions for the photoelectrocatalytic oxidation of phenol was conducted. The phenol oxidation mechanism was elucidated via high-performance liquid chromatography (HPLC) analysis and the identification of intermediate compounds. Additionally, a mixed model of phenol and arsenic (III) in polluted water demonstrated the capability of WO3/MXene photoanode for the simultaneous oxidation of both organic and inorganic pollutants, achieving complete conversion of phenol and As(III) to non-toxic As(V). The WO3/MXene photoanode facilitated water oxidation, generating a substantial amount of O2•– and •OH oxidative species, which are crucial for the concurrent oxidation of phenol and arsenic. Recyclability tests demonstrated a 99% retention of performance, confirming the WO3/MXene photoanode's suitability for long-term operation in PEAOPs. The findings suggest that integrating WO3/MXene photoanodes into water purification systems can enhance economic feasibility, reduce energy consumption, and improve efficiency. This PEAOP offers a viable solution to the critical issue of heavy metal and organic chemical pollution in various water bodies, given its scalability and ability to preserve ecosystems while conserving clean water resources.

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.

Immobilization study of a monomeric oleate hydratase from Rhodococcus erythropolis

AbstractThe chemical, pharmaceutical, and cosmetic industries are currently confronted with the challenge of transitioning from traditional chemical processes to more sustainable biocatalytic methods. To support that aim, we developed various heterogeneous biocatalysts for an industrially relevant enzyme called oleate hydratase that converts oleic acid to 10-hydroxystearic acid, a fatty emollient substance useful for various technical applications. We used cheap support matrices such as silica, chitosan, cellulose, and agarose for further scale-up and economic feasibility at the industrial level alongside more sophisticated supports like metal–organic frameworks. Different physical and chemical binding approaches were employed. Particularly, by immobilizing oleate hydrates on a 3-aminopropyltriethoxysilane surface-functionalized cellulose matrix, we developed an enzyme immobilizate with almost 80% activity of the free enzyme. The long-term goal of this work was to be able to use the developed heterogeneous biocatalyst for multiple reuse cycles enabling profitable biocatalysis. Despite high initial conversion rate by the developed cellulose-based immobilizate, a depletion in enzyme activity of immobilized oleate hydratase was observed over time. Therefore, further enzyme modification is required to impart stability, the optimization of operational conditions, and the development of carrier materials that enable economical and sustainable enzymatic conversion of oleic acid to meet the commercial demand. Graphical abstract

전기도금 공정의 지능화를 위한 생산품질 예측 모형 및 공정 조건 최적화

전기도금 공정의 지능화를 위한 생산품질 예측 모형 및 공정 조건 최적화

Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades.

Treatment of greywater coming from a food court using adsorption and advanced oxidation processes

The increase in the demand for water has recrudesced water scarcity in several places of the world, which has been intensified by the lack of rainfall, generating a water crisis, being very important to evaluate the treatment to allow the reuse of greywater. This is the first study focused on the removal of pollutants present in greywater using a pretreatment system based on ozone and solar irradiation. A series of treatments were addressed to evaluate the depuration of greywater coming from the dishwashing of a university dining hall. Notable findings of the study include several aspects such as a very high colour removal using sedimentation and, filtration and flotation process, with an overall colour removal of 91.8 %. The adsorption process removed a higher amount of surfactants with dried compost as adsorbent compared to fly ash or zeolite, with surfactant removal rates of 50.8 %, 7.8 %, and 9.0 %, respectively. Eliminating oil and grease from the greywater was studied with a solar photoreactor coupled with an ozonation process and configured in different ways. When ozone was incorporated into the vertical ozonation tube, and greywater was pumped to mix it with the ozone gas to pump the resulting mix to the borosilicate tube, oil and grease removal was 84.1 %. These treatment processes offer an effective solution for removing pollutants present in greywater; however, further studies and optimisation of operational conditions are required to enhance the efficiency of the treatment.

Numerical simulation and optimization of Pervaporation process based on Heat-Mass-Flow coupling

Pervaporation (PV) has the advantages of low energy consumption and high separation efficiency, but the trade-off effect between flux and separation factor restricts its large-scale application. Based on the conservation equations of mass, momentum and heat, a transient model of the whole PV process coupled with heat-mass-flow was established in this paper. The effects of feed temperature, feed concentration and feed flow rate on heat and mass transfer in PV process were analyzed. TEOS crosslinked PDMS/PTFE-PVDF membrane was prepared by scraping coating method. The reliability of the model was verified by taking n-butanol/water separation system as an example. The concentration polarization and temperature boundary layer under different operating conditions are discussed. The effect of operating conditions on the separation performance was evaluated by response surface method, and the optimal operating conditions are obtained. The results show that the concentration polarization of the membrane surface becomes more obvious with the increase of feed concentration. The temperature boundary layer decreases with the increase of feed flow rate. Surprisedly, there is a significant temperature gradient in the membrane, and the maximum temperature difference between the two sides of the membrane is as high as 1.02 K. Diffusion in the membrane is the rate controlling factor of PV process. The contribution of operating conditions to the separation performance is in the order of feed temperature > feed concentration > feed flow rate. The best operating conditions are: feed temperature 350.2 K, feed concentration 3.8 wt% n-butanol/water and feed flow rate 23.6 L/h. This work provides a theoretical basis for the design of PV membrane materials and the optimization of PV operating conditions.

Method for assessing well interference at under-gas cap zone using CRM material balance model

Optimization of injection operation conditions is a primary task when designing the development of mature oil fields. To select optimal injectivities, solutions to the optimization problem based on a CRM (capacitance resistance model) analytical model are used. CRM models based on analytical solutions of the material balance equations of weakly compressible fluids due to their speed can be used as an alternative to flow simulation models in solving a number of problems to support oil field development. The main task of CRM models is to determine the well interference factor, i.e. the share of fluid produced due to a particular injection well. These factors can be used to analyze waterflooding and develop solutions for waterflood optimization. At the same time, in some cases reservoir fluids may have high gas content, which is a classical limitation of a CRM model and leads to underestimation of the interference factors when estimating the injection performance. In this paper, the authors propose an approach to solve the problems of distortion of such factors, and for the first time in CRM application practice a model has been adapted for use in the conditions of high GORs and has a potential for being applied for under-gas cap zones. This is achieved by including in the equations the physical properties of gas and their behavior under reservoir conditions. The complication of algorithms did not significantly affect the model run time, but allowed to match the model both separately and jointly for liquid and gas phases. Thus, the improved classical CRM model has significantly expanded the scope of application of operational waterflood analysis tools for assessing the current situation. No less complicated and urgent is the problem of forecasting the production of gas liberated in the conditions of the previously mentioned under-gas-cap zones, because it implies the need to estimate the future reservoir pressure. The authors intend to devote the next paper to finding a solution to this issue, taking into account the proposed new method of reservoir pressure estimation using a CRM model.

Operating condition optimization of liquid metal heat pipe using deep learning based genetic algorithm: Heat transfer performance

Liquid metal heat pipes play a critical role in various high-temperature applications, with their optimization being pivotal to achieving optimal thermal performance. In this study, a deep learning based genetic algorithm is suggested to optimize the operating conditions of liquid metal heat pipes. The optimization performance was investigated in both single and multi-variable optimization schemes, considering the operating conditions of heat load, inclination angle, and filling ratio. The single-variable optimization indicated reasonable performance for various conditions, reinforcing the potential applicability of the optimization method across a broad spectrum of high-temperature industries. The multi-variable optimization revealed an almost congruent performance level to single-variable optimization, suggesting that the robustness of optimization method is not compromised with additional variables. Furthermore, the generalization performance of the optimization method was investigated by conducting an experimental investigation, proving a similar performance. This study underlines the potential of optimizing the operating condition of heat pipes, with significant consequences in sectors such as high temperature field, thereby offering a pathway to more efficient, cost-effective thermal solutions.

Intermittent investigations on attenuation mechanism of rechargeable zinc-air batteries during charge/discharge cycles.

Rechargeable zinc-air batteries (RZABs) are an ideal substitute for energy storage, but the short cycle longevity during long-term charge/discharge operation is one of the bottleneck factors that seriously restrict commercial application. Herein, the FeCo alloy/N, S co-doped carbon aerogel (NSCA/FeCo) were prepared as catalysts of cathode for RZABs. We investigated the polarization and impedance on long-term cycles during the battery operation to explore the attenuation mechanism. The results indicated that the roundtrip efficiency of batteries during charge/discharge cycles reduced fast initially and then slow. Besides, the comparative experiment was tested through the replacement of a new electrolyte and a zinc sheet. It is manifested that the failure of the battery is mainly due to the attenuation of the air cathode performance. Therefore, to further disclose the influencing factors and internal mechanisms of air cathode performance degradation, we conducted a series of characterization and testing, including the hydrophilicity, surface morphology, elemental composition, and electrochemical performance of three-electrode systems at different cycle times. This work not only provides a theoretical basis for deeply comprehending the attenuation mechanism of the cathode but also serves a reference for the material design and operating condition optimization of RZABs.

Re-evaluating the impact of feeding shock on anaerobic co-digestion of food waste and waste activated sludge: Trade-off between efficiency and stability

To optimize the feeding strategy of anaerobic digestion (AD) treating food waste and waste activated sludge, the impact of relative feeding shock (RFS) on the trade-off between efficiency and stability was re-evaluated at six feeding conditions from 1/96 (approximate continuous feeding) to 1. The results showed that increasing the RFS from 1/96 to 1/3 increased the CH4 yield from 293.23 mL CH4/g-total solid (TS) to the maximum value of 345.11 mL CH4/g-TS, which was due to the highest specific microbial growth rates (μmax) of acetate (1.05d-1) and propionate (0.48d-1) and a balanced reaction rate between acidogenesis and methanogenesis. However, when the RFS increased to 1, the total volatile fatty acids (TVFA) concentration increased from 0.51 to 5.90 g-COD/L, with the predominant VFAs changing from acetate to propionate, limiting methanogenesis. Meanwhile, the stability coefficient decreased to 0.37, leading to decreased CH4 yield and μmax. In addition, the high hydrogen partial pressure caused by high RFS increased the Gibbs free energy by 17 %, aggravating the thermodynamic disadvantages. The abundant methanogens shifted from Methanosaeta to Methanosarcina, which cooperated with a syntrophic partner-Syntrophomonas to ensure the smooth VFAs degradation at RFS of 1/3. These findings provide prima facie insights into the optimization of operational conditions for AD treating organic wastes.