Operating Conditions Research Articles

Heat transfer and friction factor analysis for tomato puree flowing in a concentric-tube heat exchanger.

The heat and momentum transfer of tomato puree through a concentric-tube heat exchanger over a range of generalized Reynolds number (0.05<Re<66.5) was experimentally and numerically analyzed. Thermophysical and rheological properties of tomato puree (12°Brix) were measured from 20 to 60°C. The velocity, pressure, and temperature were calculated using the computational fluid dynamics (CFD) software FLUENTTM with temperature-dependent transport properties. The thermal operation of the concentric-tube exchanger was satisfactorily predicted using CFD, indicating accurate measurement of tomato puree properties with temperature variations. A concordance was found between the calculated Fanning friction factor and generalized Reynolds with the experimental correlation. A modified Sieder-Tate correlation was established, which allows properly expressing the Nusselt number as a function of the Peclet number. Simple correlations for the mechanical work and the heat transfer rate as a function of the volumetric flow rate were derived. The thermal efficiency was high at low puree flow rates but decreased with higher rates. However, at high flow rates, ceased its decline, instead showing a slight improvement. The analysis confirmed higher heat transfer rates in the concentric-tube heat exchanger compared to a plain tube at low puree flow rates. The results offer valuable insights for assessing diverse operational conditions in dairy, beverage, sauce, and concentrated food industries. Additionally, they also enhance the analysis and design of concentric-tube heat exchangers. PRACTICAL APPLICATION: The knowledge of the rheological and hydrodynamical behavior of fluids in concentric-tube heat exchangers allows to explore a set of different operating conditions to improve the yield and effectiveness on the system heating/cooling design.

Roles of silica coating on nanosized zero-valent iron in sequential reduction–oxidation process in a system containing persulfate

A sequential reduction–oxidation process using silica-coated nanosized zero-valent iron (nZVI) particles (nZVI@SiO2) and persulfate for mineralizing recalcitrant compounds was developed, and the effects of the process on nitrobenzene were evaluated. This sequential process significantly enhanced contaminant mineralization, which could not be effectively achieved by reduction or oxidation alone. The nZVI@SiO2 rapidly reduced nitrobenzene to aniline, then the aniline concentration gradually decreased after persulfate had been added and initiated sequential oxidative degradation. The SiO2 coating on the nZVI@SiO2 limited outward mass transfer of reaction products and increased the efficiency with which nitrobenzene was converted into aniline. Slow release of Fe(II) caused by the coating caused persulfate activation and subsequent aniline oxidation to be more sustained and efficient than without the coating. The final nitrobenzene–aniline mineralization efficiency was higher for the nZVI@SiO2/persulfate system than the nZVI/persulfate system. The SiO2 coating of the nZVI@SiO2 particles was an excellent protective layer, protecting the particles from undesirable consumption through reactions with groundwater components. nZVI@SiO2 particle transformations during the sequential process were investigated, and the operating conditions were optimized to maximize the recalcitrant compound removal efficiency. The results indicated that nZVI@SiO2 and persulfate could be used to mineralize organic contaminants in groundwater through sequential reduction–oxidation.

Adaptive safe reinforcement learning‐enabled optimization of battery fast‐charging protocols

AbstractOptimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.

An optimal goose lithium-ion batteries accurate and rapid RUL prediction method with automatic initial hyperparameters settings

Abstract Machine learning has emerged as a highly effective tool for addressing complex data problems, garnering significant attention in the field of equipment degradation and remaining service life prediction. Existing prediction models typically exhibit two primary shortcomings: on the one hand, the accuracy of life prediction reaches the desired level of precision while failing to achieve a sufficiently fast prediction speed, and on the other hand, generalization is not guaranteed while requiring the model to be robust. These two aspects present a significant challenge to the field of machine learning. In light of the aforementioned issues, we propose a prediction model based on the goose algorithm. Initially, we set the goose algorithm using adaptive initialization of the goose population to guarantee that the goose population is set at the appropriate interval, and we incorporate it into the extreme learning machine model through the improved goose algorithm. goose algorithm is used to predict the service life. Finally, we utilize different types of lithium batteries with varying operational conditions to conduct pertinent case studies to validate the proposed prediction model. The results demonstrated that the average accuracy was above 98% in all validated datasets. The shortest computation time was 0.19 s.

Efficient Pulp and Paper Wastewater Treatment Using Green Tea-Synthesized Magnetite: Artificial Intelligence and Kinetic Analysis

The pulp and paper industry generates pollutant-laden wastewater, requiring effective treatment to prevent environmental damage and enable sustainable reuse. This study investigated the use of green tea-synthesized magnetite (GT-Fe3O4) for treating pulp and paper wastewater. Characterization techniques, including SEM, XRD, FTIR and EDX, were employed to analyze the synthesized magnetite. Batch adsorption studies were conducted to examine the optimal operating conditions, including pH, contact time, adsorbent dose, and stirring rate, for achieving the best treatment performance. The treatment focused on reducing chemical oxygen demand (COD), total suspended solids (TSS), and turbidity in the wastewater. The adsorption data were evaluated using kinetic models, with the pseudo-second-order model providing the best fit. Additionally, an artificial intelligence prediction model (AIP) with a 4–10–1 structure was employed to predict the removal efficiencies. The optimal conditions for the treatment process were determined to be a pH of 6, a contact time of 60min, a GT-Fe3O4 dose of 0.7g/L, and a stirring rate of 150rpm at room temperature. Under these conditions, the removal efficiencies for COD, TSS, and turbidity were 70.5%, 65.4%, and 38.8%, respectively. The AIP model achieved an R² value range of 0.978 to 0.982, showing high prediction accuracy. The study demonstrates that GT-Fe3O4 effectively reduces pollutants in pulp and paper wastewater.

Kinetic modeling of palmitic acid hydrodeoxygenation incorporating phase-equilibria predictions from the GC-PC-SAFT equation of state

In this work, we investigated the phase equilibria and the kinetics of palmitic acid hydrodeoxygenation over Pt/C to produce liquid hydrocarbons as drop-in biofuels. To describe the reaction mixture in detail, the binary interaction parameters of water/hydrogen, water/palmitic acid, water/n-hexadecane, and water/hexadecan-1-ol were estimated for a group contribution model based on the PC-SAFT equation of state using experimental solubility data taken from literature. Kinetic modeling using a power law model based on concentrations, a power law model based on fugacities, and a coupled VLE/power-law model were conducted to evaluate the effects of considering the non-ideality and the phase equilibria of the system. Under the operational conditions studied, the power law model based on concentrations was deemed more suitable to describe the process since it provided a faster implementation and similar outcomes compared to the fugacity-based and the VLE coupled models.

Novel Approach of Tackling Wax Deposition Problems in Pipeline Using Enzymatic Degradation Process: Challenges and Potential Solutions

Anthropogenic activities have led to hydrocarbon spills, and while traditional bioremediation methods are costly and time-consuming, recent research has focused on engineered enzymes for managing pollutant. The potential of enzymes for resolving wax flow problems in the petroleum industry remains unexplored. This paper offers a comprehensive review of the current state of research activities related to the bioremediation of petroleum-polluted sites and the biodegradation of specific petroleum hydrocarbons. The assayed enzymes that took part in the degradation were discussed in detail. Lipase, laccase, alkane hydroxylase, alcohol dehydrogenase, esterase, AlkB homologs and cytochrome P450 monooxygenase are among the enzymes responsible for the degradation of more than 50% of the hydrocarbons in contaminated soil and wastewater and found to be active on carbon C8 to C40. The possible biodegradation mechanism of petroleum hydrocarbons was also elucidated. The enzymes’ primary metabolic pathways include terminal, subterminal, and ω-oxidation. Next, given the successful evidence of the hydrocarbon treatment efficiency, the authors analyzed the opportunity for the enzymatic degradation approach if it were to be applied to a different scenario: managing wax deposition in petroleum-production lines. With properties such as high transformation efficiency and high specificity, enzymes can be utilized for the treatment of viscous heavy oil for transportability, evidenced by the 20 to 99% removal of hydrocarbons. The challenges associated with the new approach are also discussed. The production cost of enzymes, the characteristics of hydrocarbons and the operating conditions of the production line may affect the biocatalysis reaction to some extent. However, the challenges can be overcome by the usage of extremophilic enzymes. The combination of technological advancement and deployment strategies such as the immobilization of a consortium of highly thermophilic and halotolerant enzymes is suggested. Recovering and reusing enzymes offers an excellent strategy to improve the economics of the technology. This paper provides insights into the opportunity for the enzymatic degradation approach to be expanded for wax deposition problems in pipelines.

STAKgram: a method for optimal demodulation band selection in bearing fault diagnosis under complex interference

Abstract Within rotating machinery, bearings constitute critical and vulnerable components. When they operate in a compromised state, they generate periodic shock pulses. While spectral kurtosis (SK) has been demonstrated as an effective tool for pulse detection, in complex operating environments, the vibration signals of damaged bearings acquired through sensors often contain a variety of interfering pulses, such as ambient background noise, episodic single pulses, which cause SK to produce poor computational results. In this paper, an innovative bearing fault diagnostic method named subband trimmed average kurtogram (STAKgram) is proposed, aiming to minimize the effect of interfering signals on signal processing results, thus ensuring reliable diagnostic results even in complex interference environments. Firstly, the signal is segmented into N sub-signals by employing a sliding window method. Following, the subband kurtosis is calculated for every sub-signal. Ultimately, the trimmed average kurtosis of the respective sub-band is computed to construct the STAKgram, indicating the optimal demodulation frequency band. It is used for squared envelope demodulation analysis. By simulation analysis and the application of test signals obtained under conditions closely resembling authentic bearing operating conditions, the STAKgram method is proven to provide more accurate results for diagnosing bearing faults in complex interference.

Multifactor evaluation method of smart meter

As an important terminal of smart grid, smart meter has the functions of equipment health monitoring and power metering. However, it is difficult to quantify the impact of factors such as ex-factory parameters, historical failure rates, electromagnetic interference when conducting an operational condition assessment. Therefore, in view of the large number of influencing factors and the difficulty in quantifying the degree of influence, an improved multifactor evaluation model for the operating status of smart meters is constructed. Firstly, a multifactor index system for evaluating the operating state of smart meters is constructed by analyzing the working mechanism and operating characteristics of smart meters. Then, an improved combinatorial weighting method is proposed by combining the subjective weight and the objective weight. The combinatorial weighting method is used to estimate the weight of factors in the health status evaluation of smart meters, and the improved multifactor evaluation method is presented to evaluate the health status of smart meters. Finally, an example is given to verify and analyze the proposed method. Compared with the other three methods, this method can provide effective evaluation results, and help guide targeted maintenance or replacement to improve the efficiency of smart meter detection.

Mathematical Model of Drilling Mud Movement along a Shale Shaker Screen

Introduction. When drilling oil or gas wells, rock from the bottomhole is brought to the surface by drilling mud, which is cleaned of sludge by shale shakers at the first stage. Shale shakers are equipped with such a screen and create such trajectories of vibration of the frame in order to solve a dual problem: on the one hand, to provide the cleaning of the drilling mud coming from the well mouth, and on the other hand — to maintain the proper degree of cleaning. To correctly select the nomenclature of screens, it is necessary to reliably determine the throughput capacity of a shale shaker in the real-time setting. This will allow you to pre-order the required number of screens with the required cell size. Previously, studies were conducted by sieving a mixture of calibrated granules on a standard shaker or by straining mineral oil through a fixed screen. However, this does not fully correspond to the actual conditions of drilling mud screening. The objective of this article was to simulate the throughput of a drilling shale shaker under real conditions by calculating the movement of drilling mud along a vibrating screen using its previously obtained specific throughput capacity.Materials and Methods. When creating a mathematical model of the full throughput capacity of a shale shaker, a previously known mathematical model of fluid flow in an open channel and a finite-dimensional approximation in the form of a sequence of sections of concentrated containers connected by hydraulic conductivities (by analogy with the approximation of long electrical lines and extended gas pipelines by the finite element method) were used. The fluid flow rate over the screen was determined according to Chézy law. In this case, the cross-sectional area of the flow above the screen for a specific section was specified as the arithmetic mean between the initial and final values of the drilling mud height in the corresponding section.Results. A chain calculation scheme for sieving drilling mud was compiled. Based on the material balance, mathematical models were developed for determining the throughput and height of drilling mud above the screen: for the first section of the screen and the first nodal point of the screen; for subsequent screen sections and subsequent screen nodal points; for the last section of the screen and the last nodal point of the screen.A mathematical model of the drilling mud movement along the shale shaker screen has been obtained, which, due to the finite-dimensional representation of this flow along n sections of the screen length, is a system of n integral equations describing concentrated tanks, and n – 1 algebraic equations describing the flow of drilling mud between tanks.Discussion and Conclusion. The movement of drilling mud along the length of the shale shaker screen corresponds to the movement of a steady uneven flow in an open channel. However, in the first case, the volume flow along the length is variable (decreasing to zero), and in the second case, it is constant along the length. Therefore, the use of only one differential equation for steady uneven flow in an open channel is not sufficient in this case. The resulting mathematical model of the drilling mud movement along the shale shaker screen is a mathematical basis for the development of a modeling program to determine the throughput of the shale shaker for real operating conditions, i.e., for a specific drilling mud, a given screen or a given drive system of the shale shaker.

Enhancing sludge thickening in continuous treatment using polymeric bubbles with cationic polymer P2900 and cocamidopropyl betaine.

Sludge thickening is a fundamental stage of treatment. This study investigated the application, in continuous treatment, of polymeric bubbles produced with cationic polymer P2900 and cocamidopropyl betaine (CAPB), a zwitterionic surfactant. The proposed reagent combination aims to form aerated flakes, solid waste structures, and rapidly rising air bubbles, ideal for treatments in compact units. Using this combination, it was possible to achieve a total solids concentration of 45% with the modified bubbles and 25% with the conventional water treatment. This level of thickening occurred under the following operating conditions: initial total solids (TS) concentration of 10g L-1, a flow rate of 5 L min-1, saturation pressure (psat) of 3atm, and polymer dosage of 10mg (gTS)-1. The suggested mechanism of action involves the adhesion of P2900 molecules to CAPB at the air/water interface, forming a lining on the bubble surface. Additionally, polymerized species form due to the residual aluminum (Al) in the sludge, which would occur during flocculation in the helical tubular flocculator (HTF), adsorbing the micelles and bubbles of CAPB. The critical micellar concentration (CMC) of CAPB was 0.26mmol L-1. Polymeric bubble technology can provide an efficient and cost-effective approach to sludge thickening in continuous treatment.

Reliability inference for dual constant-stress accelerated life test with exponential distribution and progressively Type-II censoring

Accelerated life test provides a feasible and effective way to rapidly derive lifetime information by exposing products to higher-than-normal operating conditions. However, most of the previous research on accelerated life test has focused on the application of a single stress factor and a traditional censoring scheme. This article considers the reliability inference for a dual constant-stress accelerated life test model with exponential distribution and progressively Type-II censoring. Point estimates for model parameters are provided using maximum likelihood estimation and the weighted least squares method based on random variable transformation. In addition, we construct asymptotic confidence intervals, approximate confidence intervals, and bootstrap confidence intervals for the parameters of interest. Finally, extensive simulation studies and an illustrative example are presented to investigate the performance of the proposed methods.

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.

Advanced controller design based on interval type-2 fuzzy neural network for elementary super-lift Luo converter

The elementary super-lift Luo converter is a third-order DC/DC step-up converter developed using the super-lift method that increases input voltage with low current and voltage ripples and a high voltage gain. On account of the nonlinearity and uncertainty incorporated with voltage, load changes, and switching operation of elementary super-lift Luo (ESLL) converter, it is a challenging task to design an efficient controller. To overcome these problems, this study proposes a new control approach based on interval type-2 fuzzy neural network (IT2FNN) to regulate a DC output current and voltage of ESLL converter. The parameters of IT2FNN were trained offline using a gradient descent algorithm. Mean square error (MSE), one of the most used statistical performance indicators, was used to evaluate the convergence accuracy performance of gradient descent algorithm. The DC output voltage regulation performance of IT2FNN was evaluated via a comparative simulation with interval type-2 fuzzy controller (IT2FC) and proportional + integral (PI) controller in MATLAB/Simulink environment in terms of settling time, average computational time, and recovery time under three different operational conditions: various reference voltages, input voltage, and load. Moreover, the DC output current regulation performance of IT2FNN was evaluated via a comparative simulation with IT2FC and PI controller in MATLAB/Simulink environment in terms of settling time and steady-state error under various reference current changes. The detailed simulation results obtained from comparative simulation validated the effectiveness of IT2FNN.

Enhanced Glycerolysis of Fatty Acid Methyl Ester by Static Mixer Reactor.

This study investigates the synthesis of monoglycerides (MGs) and diglycerides (DGs) from glycerol (G) and fatty acid methyl ester (FAME) using a static mixer reactor (SMR), which combines a static mixer (SM) with a reactor tank. The SMR integrates Kenics static mixers (KSM) and low-pressure drop static mixers (LPDSM) with varying length-to-diameter ratios (L/D = 1.0 and 1.5). Keys glycerolysis parameters, including the G:FAME molar ratio of 2:1-3:1, 2-3 wt % potassium hydroxide (KOH), and reaction time of 30-90 min at 150 °C were systematically explored. The SMR design allows precise control over the reaction time without altering the feed flow rate or tube length and avoiding agitator leakage. The optimal operating conditions, determined through a face-centered central composite design, resulted in 71.35% MGs and 14.20% DGs at a 3:1 molar ratio of G to FAME, 3 wt % KOH, 60 min, and 150 °C using an LPDSM with an L/D of 1.5. In comparison, an LPDSM with an L/D of 1 achieved 79.28% MGs and 10.17% DGs under the same conditions. When applied to purified crude glycerol, these conditions yielded 61.09% MGs and 23.44% DGs. The study found that a lower L/D ratio improved the mixing efficiency but increased the pressure drop. The SMR demonstrated superior performance in glycerolysis compared with conventional stirred tank reactors and ultrasonic probe reactors, indicating its potential for enhanced industrial application.

ANFIS-RTSMC based MPPT for a hybrid PV-diesel-battery water pumping system

This paper introduces a novel hybrid method for achieving Maximum Power Point Tracking (MPPT) in a photovoltaic-diesel-battery hybrid water pumping system driven by an induction motor drive (IMD). The innovative MPPT control strategy uniquely combines the rapid response and minimal oscillations of the Adaptive Neuro Fuzzy Inference System with the high robustness of Robust Terminal Sliding Mode Control (RTSMC). This combination ensures unprecedented steady-state resilience to changes in solar irradiance, enhancing system stability across a variety of IMD speeds, and making the system robust to parameter variations. Unlike traditional approaches, this two-stage photovoltaic (PV) setup integrates a regulated boost converter for MPPT control and a three-phase inverter using Space Vector Pulse Width Modulation (SVPWM) to regulate the DC link voltage (Vdc). Extensive MATLAB Simulink simulations demonstrate the proposed method’s superior efficiency in power extraction and its resilience under varying operating conditions, showcasing its originality and effectiveness.

Analysis and estimation of power losses in high-power, high-frequency transformers for isolated DC/DC converters

The paper presents an in-depth analysis of the power losses in the windings and the cores of two different transformers as applied to a phase-shifted full bridge (PSFB) converter at the power level of 20–25 kW and the switching frequency of 50 kHz. The main difference in the construction of the considered devices was the method of winding realization as well as the shape and material of the cores. The influence of the winding geometry on the conduction losses was analyzed, and the losses in the cores were analytically estimated, taking into account the operating conditions of these elements in the full-bridge system with phase-shift modulation. Original resistive models of windings made of cooper sheets are presented. The obtained results were verified by experimental tests carried out in an isolated full-bridge DC/DC converter.

Функционирование предприятий промышленности России в современных условиях: проблемы и возможности

The study is devoted to the analysis of the operating conditions of industrial enterprises in Russia. The relevance of the study is emphasized by the period of economic instability in the Russian economy, which revealed the insufficient efficiency of industrial enterprises, expressed in low profitability of production. The purpose of the study is to analyze and systematize factors, problems and promising directions that determine the functioning of an industrial enterprise, and to develop measures to achieve economic sustainability in an unstable external environment. As a result of the study, the main factors for the functioning of industrial enterprises in an unstable external environment were identified, and measures were proposed to increase the efficiency of enterprises, such as increasing energy intensity and developing exports.

A Bearing Fault Diagnosis Method in Scenarios of Imbalanced Samples and Insufficient Labeled Samples

In practical working environments, rolling bearings are one of the components that are prone to failure. Their vibration signal samples are faced with challenges, mainly including the imbalance between normal and fault samples as well as an insufficient number of labeled samples. This study proposes a sample-expansion method based on generative adversarial networks (GANs) and a fault diagnosis method based on a transformer to solve the above issues. First, selective kernel networks (SKNets) and a genetic algorithm (GA) were introduced to construct a conditional variational autoencoder–evolutionary generative adversarial network with a selective kernel (CVAE-SKEGAN) to achieve a balance between the proportion of normal and faulty samples. Then, a semi-supervised learning–variational convolutional Swin transformer (SSL-VCST) network was built for the fault classification, specifically introducing variational attention and semi-supervised mechanisms to reduce the overfitting risk of the model and solve the problem of a shortage of labeled samples. Three typical operating conditions were designed for the multi-case applicability verification. The results show that the method proposed in this study had good application effects when solving both sample imbalances and labeled-sample deficiencies and improved the accuracy of fault diagnosis in the above scenarios.

Experimental Study on Heat Recovery in a CaO/Ca(OH)2-Based Mechanical Fluidized Bed Thermochemical Energy Storage Reactor

Long-term storage of seasonally available solar energy and its provision to balance heating energy demand can contribute significantly to the sustainable use of energy resources. Thermochemical energy storage is a suitable process for this purpose, offering the possibility of loss-free long-term energy storing and heat supply. In order to develop suitable technical solutions for the use of this technology, novel reactor concepts and scientific questions regarding material and technology development are being investigated. In this publication, the energy storage process of a long-term energy storage system based on a ploughshare reactor is experimentally investigated under various technically relevant operating conditions. One specific aspect of this technology is related to the release of water vapour during the charging process. Therefore, this work focusses, in particular, on the possibility of technically utilizing the latent heat of the released water vapour in the range of 45 °C to 80 °C, which covers the operating requirements of common heating systems in households. The experiments have shown that the dehydration process enables the separation of two heat fluxes: the chemically bound energy for long-term storage and the physically (sensible and latent) stored energy for short-term applications. However, the limitation of gas transport was also identified as the most important influencing parameter for optimising the performance of the process.