Operating Conditions Research Articles

Effect of liquid-vapor two-phase flow maldistribution on the thermal performance of brazed plate heat exchangers

The effect of liquid-vapor two-phase flow maldistribution on the thermal performance of brazed plate heat exchangers (BPHEs) is investigated experimentally and numerically. The two-phase refrigerant flow distributions among different channels of the BPHEs are first experimentally quantified based on the infrared radiation images of the heat exchanger sidewalls. To assess the performance degradation due to two-phase flow maldistribution, the thermal performance of the maldistributed BPHEs is measured and compared with that of the uniform-distributed BPHEs, predicted using an experimentally validated BPHE model. Several factors that affect the performance degradation are analyzed, including refrigerant inlet vapor quality and flow rate, exit superheat, and water-side flow distribution. The results show that, under the tested conditions, the two-phase refrigerant flow maldistribution causes up to a 29.8 % degradation in heat exchanger capacity and a 4 K decrease in evaporation temperature. Furthermore, the vulnerability of the BPHEs to two-phase flow maldistribution is significantly influenced by the operating conditions. This is closely related to the varying size ratios of superheated and two-phase regions within the BPHEs under different operating conditions. Reducing the refrigerant exit superheat is verified to help overcome the adverse effect brought by the flow maldistribution in the evaporators, but brings challenges in the system control. Matching the flow distributions of liquid refrigerant and water also proves beneficial to the BPHE evaporators.

Ultrasound assisted efficient separation of lithium from brine with a composite polyether sulfone-ionic liquid membrane.

Green, selective and efficient extraction of lithium as one of the most important components for energy storages with ultrasound-assisted membrane separation of lithium from brine, which contains alkali metal chlorides, is conducted using a composite membrane. The composite membrane is formed by sealing a supported ionic liquid membrane (consisting of 1-alkyl-3-methylimidazolium hexafluorophosphate ([RMIM][PF6]) + TBP) with a polyethersulfone (PES) membrane and a PVC thin film membrane. The aim of the study is to optimize the separation process for the selective extraction of lithium from alkali metals. Various parameters, including membrane composition, feed concentration, and ultrasonic conditions, are adjusted to identify the best operating conditions. The results reveal that a membrane containing xIL = 0.5 of [MOIM][PF6] exhibits higher selectivity compared to other membranes studied. The flux of lithium initially increases with shorter sonication times, but it decreases as the duration of ultrasonic irradiation is prolonged. The optimal frequency for the ultrasonic treatment, which matches the bulk modulus of the membrane, is approximately 250 kHz. Higher frequencies result in higher flux and selectivity in lithium separation; besides, optimizing the amplitude and pulse cycle of the ultrasound at 75% leads to increased flux. Moreover, higher flux and selectivity (percentage of lithium with respect to the all of the ion flux) are achieved when separating lithium from alkali metal chlorides at higher feed concentrations, ranging from 250 ppm to 1000 ppm. The selectivity is influenced by the hydrophobicity, which depends on the behavior of the ionic liquid membrane. The process is promising for the future of the lithium mining from brine.

An Aero Engine Disk Sizing and Weight Estimation Approach with Improved Temperature Profiles

Abstract During the predesign phase of an aero engine, interdisciplinary parametric studies are crucial for identifying an optimum engine. As innovative engine concepts emerge, it is essential to study trends aiming to minimize climate impact and one of its key contributors, fuel burn. The comparatively heavy disks, subjected to high thermal and mechanical loads, are central to this analysis due to their high impact on the overall module mass. This paper proposes an approach to achieve a first lightweight mechanical design of disks in the predesign phase. Based on a performance and aerodynamic baseline, the design space and physical boundary conditions are set. Firstly, an initial mass optimized disk is derived using simplified temperature profiles and stress calculations under the Mechanical Design Point conditions. Subsequently, all the module disks are considered and an estimation for each bore temperature is achieved through a novel loop over the entire module and all available operating points. By evaluating the temperature and stress profiles across various operating conditions, the lowcyclefatigue life of each disk can be studied. As part of a highly modular engine predesign framework, this method allows for zooming capabilities of single disk calculations by prescribing individual boundary conditions. The proposed approach already yields an initial assessment of the rotors within a short runtime, facilitating simple iterations with the succeeding design phases due to method and tool commonalities. While the primary focus of the current work is on compressor design, the principles presented are adaptable and expandable to turbine disk applications as well.

On the Influence of Engine Compression Ratio on Diesel Engine Performance and Emission Fueled with Biodiesel Extracted from Waste Cooking Oil

Despite the extensive research on biodiesels, further investigation is warranted on the impact of compression ratios on emissions and engine performance. This study addresses this gap by evaluating the effects of increasing the engine’s compression ratio on engine performance metrics—brake-specific fuel consumption (BSFC), power, torque, and exhaust gas temperature—and emissions—unburnt hydrocarbons (HCs), carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and oxygen (O2)—when fueled with a 20% blend of waste cooking oil biodiesel (WCB20) and petroleum diesel (PD) under various operating conditions. The viscosity of the prepared fuels was measured at 25 °C and 40 °C. Experiments were conducted on a single-cylinder diesel engine under wide-open throttle conditions at three different speeds (1400 rpm, 2000 rpm, and 2600 rpm) and two compression ratios (16:1 and 18:1). The results revealed that at a lower compression ratio, both WCB20 and petroleum diesel exhibited reduced BSFC compared to higher compression ratios. However, increasing the compression ratio from 16:1 to 18:1 significantly decreased HC emissions but increased CO2 and NOx emissions. Engine power increased with engine speed for both fuels and compression ratios, with WCB20 initially producing less power than diesel but surpassing it at higher compression ratios. WCB20 demonstrated improved combustion quality with lower unburnt hydrocarbons and carbon monoxide emissions due to its higher oxygen content, promoting complete combustion. This study provides critical insights into optimizing engine performance and emission characteristics by manipulating compression ratios and utilizing biodiesel blends, paving the way for more efficient and environmentally friendly diesel engine operations.

A novel efficient calculation method for rotor aeroacoustic characteristics based on multiple aerodynamic surfaces source model

Based on the simplified acoustic source model of multiple aerodynamic surfaces, an efficient calculation method for rotor aeroacoustic characteristics has been established. Firstly, the aerodynamic characteristics database of the airfoil is obtained based on the Computational Fluid Dynamics (CFD) method. The inflow environment of each blade element is calculated using the free wake method, and the distribution of chord-wise loading is obtained by combining the established database. Subsequently, based on the F1A equation, a simplified formula for loading noise with pressure difference as a parameter is derived. The thickness noise is obtained from the blade shape and operating conditions, and the blade surface mesh is constructed by using the adaptive curvature distribution strategy of airfoil points. The validation is carried out by comparing with experimental values in the hover and forward flight states. Then, A hybrid weighted interpolation scheme is proposed based on inverse distance weighted interpolation (IDW) and bidirectional linear interpolation method, which is suitable for capturing blade/vortex interaction (BVI) noise. A set of parameters suitable for engineering applications is obtained through the parametric sensitivity analysis. The computational time and memory usage are reduced to 1/47 and 1/4 of the original amount, respectively. Finally, Using the above method, the influence of rotational speeds (ω) on rotor aeroacoustic characteristics are studied. The results show that ω and sound pressure level (SPL) are linearly related and the SPL decreases by approximately 5 dB for every 0.1 Ω decrease in ω. The rotational speed ω≈90 %Ω can effectively suppress rotor aeroacoustic levels, benefiting the stealth design of helicopters.

Process–Property Correlation in Sustainable Printing Extrusion of Bio-Based Filaments

This study investigated the effect of two critical variables for environmental process sustainability, i.e., extruder temperature and printing rate, on thermomechanical performance and accuracy in overall sample sizes, when printing bio-based materials. In this context, 3D specimens produced from basic polylactide (n-PLA) and wood-filled PLA polymer (f-PLA) were realized using extrusion-based additive manufacturing technology (MEX) by varying the nozzle temperatures (200 °C, 210 °C, and 220 °C) and speed (from 70 mm/s to 130 mm/s). Dynamic mechanical analysis (DMA) was carried out on the produced specimens, providing information on changes in storage modulus at testing temperature of 30 °C (E′30) and glass transition temperature (Tg) for each printing condition. Measurements of sample sizes allowed for printing precision considerations as a function of processing temperature and speed. The results revealed similar trends in E′30 changes in printed specimens at a fixed extruder temperature as a function of printing speed for n-PLA and f-PLA. Infrared spectroscopy was performed on printed samples and unextruded material to attest potential material degradation under various operating conditions. Finally, images of sample surface allowed to verify the homogeneity of the diameter of the extruded material, and the layer–layer contact at the interface.

Algorithm for Key Transparency with Transparent Logs

Background Cryptography plays a crucial role in securing digital communications and data storage. This study evaluates the Transparent Key Management Algorithm utilizing Merkle trees, focusing on its performance and security effectiveness in cryptographic key handling. Methods The research employs simulated experiments to systematically measure and analyze key operational metrics such as insertion and verification times. Synthetic datasets are used to mimic diverse operational conditions, ensuring rigorous evaluation under varying workloads and security threats. Implementation is carried out using R programming, integrating cryptographic functions and Merkle tree structures for integrity verification. Results Performance analysis reveals efficient insertion and verification times under normal conditions, essential for operational workflows. Security evaluations demonstrate the algorithm's robustness against tampering, with approximately 95% of keys verified successfully and effective detection of unauthorized modifications. Simulated attack scenarios underscore its resilience in mitigating security threats. Conclusions The Transparent Key Management Algorithm, enhanced by Merkle trees and cryptographic hashing techniques, proves effective in ensuring data integrity, security, and operational efficiency. Recommendations include continuous monitoring and adaptive algorithms to bolster resilience against evolving cybersecurity challenges, promoting trust and reliability in cryptographic operations.

Cross-domain fault diagnosis network based on attributes and features transfer with dual classifier under limited and unbalanced datasets

Abstract Deep learning (DL)-based methods have demonstrated significant success in fault diagnosis owing to their robust feature extraction and non-linear fitting capabilities. Meanwhile, their remarkable performance is accompanied by constant operating conditions and sufficient monitoring data. However, in real engineering environments, variable working conditions or limited and unbalanced data are common, which can widen the gap between fault diagnosis methods and real industrial applications. In this paper, we proposed a cross-domain fault diagnosis network based on a dual classifier (CFDNet) with input being limited and unbalanced data to learn attributes and features for unsupervised domain adaptation. We found that the diagnostic performance is commonly bounded by the underlying knowledge, especially feature extraction from original data. Therefore, we designed a new feature encoder with features and relationships, i.e. using a convolutional neural network and graph convolutional network, which improves extraction efficiency while retaining valuable information. Then, we discovered that enforced feature transfer can lead to negative transfer. To mitigate this, we present a feature and attribute transfer framework, which not only achieves features transfer but also enables attributes transfer. Furthermore, it was noted that limited and unbalanced datasets can introduce label bias and lead to biased model training. Hence, we designed dual classifiers to improve the probability of high-confidence final prediction by synthesizing diagnostic results. Comprehensive experiments conducted on three case studies demonstrate the effectiveness and superiority of our method for cross-domain fault diagnosis under limited and unbalanced datasets, which outperforms state-of-the-art methods in this study.

An operating mode control method for photovoltaic (PV) battery hybrid systems

Depending on the PV power, load power, and battery status, the system may operate in different modes. The control loop may have to switch between operating modes. In practice, it is difficult to implement control loop switching because the transition and dynamic process are difficult to control. As a result, this paper presents a generalized mode control method that avoids loop switching across modes. First, system structure and topology are introduced. The operating conditions for both grid-connected and off-grid modes are then divided into six sub-cases. Furthermore, the control architecture, control loop, and reference transition for various scenarios are described. Finally, an experimental platform is built, and the results are presented to verify the proposed method.

Drop-by-Drop Addition of Reagents to a Double Emulsion.

Developments in droplet microfluidics have facilitated an era of high-throughput, sensitive single-cell, or single-molecule measurements capable of tackling the heterogeneity present in biological systems. Relying on single emulsion (SE) compartments, droplet assays achieve absolute quantification of nucleic acids, massively parallel single-cell profiling, and more. Double emulsions (DEs) have seen recent interest for their potential to build upon SE techniques. DEs are compatible with flow cytometry enabling high-throughput multi-parameter drop screening and eliminate content mixing due to coalescence during lengthy workflows. Despite these strengths, DEs lack important technical functions that exist in SEs such as methods for adding reagents to droplets on demand. Consequently, DEs cannot be used for multistep workflows which has limited their adoption in assay development. Here, strategies to enable reagent addition and other active manipulations on DEs are reported by converting DE inputs to SEs on chip. After conversion, drops are manipulated using existing SE techniques, including reagent addition, before reforming a DE at the outlet. Device designs and operation conditions achieving drop-by-drop reagent addition to DEs are identified and used as part of a multi-step aptamer screening assay performed entirely in DE drops. This work enables the further development of multistep DE droplet assays.

Assessment of UHF Frequency Range for Failure Classification in Power Transformers

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

Effective machine lifespan management using determined state–action cost estimation for multi-dimensional cost function optimization

ABSTRACT This study introduces a comprehensive framework designed to enhance production efficiency by integrating maintenance strategies, energy costs, and production specifications. This integration is achieved through a novel empirical method for estimating state–action costs, suitable for both machines with measurable and non-measurable states-of-health. We address the challenge of under-determination in state–action cost optimization by employing a k-means clustering approach, ensuring robustness and applicability. Utilizing an adapted SARSA algorithm, our framework optimally controls shop-floor machinery to minimize the global cost function. The efficacy of the state–action cost estimation method is validated using NASA’s C-MAPSS dataset. Additionally, the optimization strategy is further corroborated through its successful implementation in an autonomous mining cart model on the shop floor. Our results highlight the framework’s ability to optimize machine lifetime and production processes effectively, providing tailored solutions that adapt to varying operational conditions without depending on predefined machine degradation models and costs.

Anti-Russian sanctions in the domestic oil and gas industry: problems and prospects

The relevance of the topic of the scientific article is determined by a number of circumstances related to the fact that the oil and gas industry is one of the main sectors of the Russian economy, as well as an important component of domestic exports. The sanctions of Western countries on the economy of the Russian Federation have had a noticeable impact on the operating conditions of the industry and the requirements imposed on it by the world and the national economy. The article formulates the challenges facing the industry, which is under sanctions, and shows the possibilities of its development in 2024 and in the future. According to the author, the effectiveness of industry management, improvement of technologies, logistics, as well as the search for non-traditional sales markets are of fundamental importance today. The purpose of the article is to identify the problems of the domestic oil and gas industry that arose in the sanctions conditions, to identify realistic directions for its development. The subject of the study is the hydrocarbon industry of the Russian Federation, the object of the study is changes in the domestic energy sector under the influence of Western sanctions. The article used analytical, comparative, empirical methods and visualization. It is shown that along with the development of traditional energy in the Russian Federation, it is important to pay close attention to the intensification of activities in the field of renewable energy sources. A number of measures have been proposed to reduce the negative impact of sanctions on the hydrocarbon segment of the domestic economy.

Analysis of the Impact of Cooling Lubricants on the Tensile Properties of FDM 3D Printed PLA and PLA+CF Materials

This study investigates the impact of infill density on the mechanical properties of fused deposition modeling (FDM) 3D-printed polylactic acid (PLA) and PLA reinforced with carbon fiber (PLA+CF) specimens, which hold industrial significance due to their applications in industries where mechanical robustness and durability are critical. Exposure to cooling lubricants is particularly relevant for environments where these materials are frequently subjected to cooling fluids, such as manufacturing plants and machine shops. This research aims to explore insights into the mechanical robustness and durability of these materials under realistic operating conditions, including prolonged exposure to cooling lubricants. Tensile tests were performed on PLA and PLA+CF specimens printed with varying infill densities (40%, 60%, 80%, and 100%). The specimens underwent tensile testing before and after exposure to cooling lubricants for 7 and 30 days, respectively. Mechanical properties such as tensile strength, maximum force, strain, and Young’s modulus were measured to evaluate the effects of infill density and lubricant exposure. Higher infill densities significantly increased tensile strength and maximum force for both PLA and PLA+CF specimens. PLA specimens showed an increase in tensile strength from 22.49 MPa at 40% infill density to 45.00 MPa at 100% infill density, representing a 100.09% enhancement. PLA+CF specimens exhibited an increase from 23.09 MPa to 42.54 MPa, marking an 84.27% improvement. After 30 days of lubricant exposure, the tensile strength of PLA specimens decreased by 15.56%, while PLA+CF specimens experienced an 18.60% reduction. Strain values exhibited minor fluctuations, indicating stable elasticity, and Young’s modulus improved significantly with higher infill densities, suggesting enhanced material stiffness. Increasing the infill density of FDM 3D-printed PLA and PLA+CF specimens significantly enhance their mechanical properties, even under prolonged exposure to cooling lubricants. These findings have significant implications for industrial applications, indicating that optimizing infill density can enhance the durability and performance of 3D-printed components. This study offers a robust foundation for further research and practical applications, highlighting the critical role of infill density in enhancing structural integrity and load-bearing capacity.

Research on Plug-in Hybrid Electric Vehicle (PHEV) Energy Management Strategy with Dynamic Planning Considering Engine Start/Stop

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine’s operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy.

Effects on the Indoor Environment in a Stable for Horses in Winter: A Case Study

The aim of this article is to show the most significant factors influencing the indoor environment in winter considering the operating conditions of an older stable modified for housing 12 horses and an indoor riding arena for teaching and sports purposes. This research focused on assessing the influences affecting the internal environment from the point of view of the construction of the building and ventilation control in the operating conditions of working and non-working days. The analysis of the results showed that the massive masonry structure has sufficient thermal insulation and accumulation, which was manifested by good temperature stability inside the stable of 7.2 ± 1.7 °C when the outside air temperature was −4.80 ± 1.5 °C. At low outdoor air temperatures of −6.44 ± 0.4 °C, the following conditions were found: a higher relative air humidity (76.0 ± 5.3%), a high CO2 concentration 2317.1 ± 931.7 ppm, and a high airborne dust concentration PM10 = 231.94 ± 19.13 μg·m−3 and PM2.5 = 160.13 ± 6.28 μg·m−3. Therefore, it is necessary to improve the solution and function of the stable ventilation. The small size of the windows and their uneven distribution (average daylight factor ei from 0.313 ± 0.154 to 0.835 ± 0.309) caused insufficient daylight in some individual boxes.

Improved Cupressus sempervirens L. galls for methylene blue removal: adsorption kinetics optimisation using the DA-LS algorithm, characterisation, and machine learning modeling

ABSTRACT In this study, an investigation was conducted on the effectiveness of three bioadsorbents – galbuli of Cupressus sempervirens L. almond shells, and Luffa – for removing methylene blue (MB) from water. Various activation techniques and operational conditions were employed to enhance the adsorption capacity. The study focused on adsorption kinetics and equilibrium capacity. The results showed that microwave-dried galbuli of Cupressus sempervirens L. exhibited the highest adsorption capacity of 38.05 mg/g with an adsorption efficiency of 17.4%. To improve this capacity, the Dragonfly Algorithm-integrated least squares (DA-LS) was proposed to optimise the fitting of 32 adsorption isotherm models, which included a proposed modified Langmuir model. After optimisation, the sixth chemical treatment of Cupressus sempervirens L. reached a Langmuir isotherm qmax value of 84.44 mg/g, suggesting the high efficiency of the bioadsorbent compared to previous studies. Characterisation using microscopy and ATR-FTIR techniques revealed that chemical modifications led to rougher, microporous cellulose fibres and significant changes in the chemical structure, potentially enhancing the surface area and adsorption efficiency. To predict the adsorption capacity, the Support Vector Machine for Regression with DA (SVMR-DA) was utilised, achieving a root mean square error (RMSE) of 1.357 and an R2 of 0.9913. This high accuracy indicates a reliable model with a robust error distribution. These results suggest that Cupressus sempervirens L. can be an effective bioadsorbent for water treatment, with significant potential for industrial application.

Estimating the Health State of Lithium-Ion Batteries Using an Adaptive Gated Sequence Network and Hierarchical Feature Construction

State of health (SOH) estimation plays a vital role in ensuring the safe and stable operation of lithium-ion battery management systems (BMSs). Data-driven methods are widely used to estimate SOH; however, existing methods often suffer from fixed or excessively high feature dimensions, impacting the model’s adjustability and applicability. This study first proposed a layered knee point strategy based on the charging voltage curve, which reduced the complexity of feature extraction. Then, a new hybrid framework called the adaptive gated sequence network (AGSN) model was proposed. This model integrated independently recurrent neural network (IndRNN) layers, active state tracking long short-term memory (AST-LSTM) layers, and adaptive gating mechanism (AGM) layers. By integrating a multi-layered structure and an adaptive gating mechanism, the SOH prediction performance was significantly improved. Finally, batteries under different operating conditions were tested using the NASA battery dataset. The results show that the AGSN model demonstrated higher accuracy and robustness in battery SOH estimation, with estimation errors consistently within 1%.

A meta-learning method for smart manufacturing: Tool wear prediction using hybrid information under various operating conditions

Accurate tool wear prediction during machining is crucial to manufacturing since it will significantly influence tool life, machining efficiency, and workpiece quality. Although existing data-driven methods can achieve competitive performance in tool wear prediction, their main emphasis is on fixed operating conditions with sufficient training samples, which is impractical in engineering practice. This implies that predicting tool wear values under variable working conditions with insufficient data is still a challenge owing to the difference in data distributions in complex tool wear mechanisms. Besides, having no access to samples in new conditions is another challenge for tool wear prediction in engineering practice. To address these issues, we develop a hybrid information model-agnostic domain generalization (H-MADG) method to provide appropriate initial model parameters that can be fast adaptative to the new conditions after fine-tuning. Additionally, we construct hybrid information as model input by fusing process information with temporal properties derived by neural networks, and the hybrid information can offer more useful prior knowledge about the machining process. Experimental results on NASA milling data show that compared with contrastive techniques, the RMSE of the proposed H-MADG method is reduced by an average of 36.81 %, which can achieve a low average RMSE value of 0.0904 with 15 cases under five different network architectures. We also investigate several crucial impact factors of the H-MADG method and summarize corresponding analysis and suggestions.

Eco-friendly solvent-based liquid-liquid extraction of phenolic acids from winery wastewater streams

This study proposes liquid-liquid extraction (LLE) for the recovery of phenolic acids from winery wastewater replacing common volatile organic compounds (VOCs) with environmentally friendly solvents. On one hand, terpenes (α-pinene and p-cymene) and terpenoids (eucalyptol and linalool) were selected as green solvents and compared to common VOCs (ethyl acetate or 1-butanol). On the other hand, gallic acid (GA), vanillic acid (VA), syringic acid (SA) and caffeic acid (CA) were selected as phenolic acids to be recovered. The extraction performance was evaluated under different operation conditions: solvent-to-feed ratio, initial concentration of phenolic acids and temperature.This work also evaluated the back-extraction whole process global recovery and solvent regeneration, by means of aqueous NaOH solution. Eucalyptol has shown the highest overall global extraction performance (21.07 % for GA, 93.21 % for VA, 78.79 % for SA, and 80.57 % for CA) and lower water solubility compared to the best performing VOC solvent (1-butanol). Therefore, eucalyptol can be a potential eco-friendly solvent to replace VOCs for sustainable phenolic acid recovery from winery wastewater. Finally, to ensure a clean water stream after the LLE, the traces of solvent were completely removed by electrooxidation with boron-doped diamond anode at a current density of 422.54 A/m2.