Operating Conditions Research Articles

Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization

Abstract The worldwide transition to cleaner, sustainable energy sources, prompted by population growth and industrialization, responds to uncertain fossil fuel prices and environmental concerns, highlighting the substantial benefits of renewable energy in reducing greenhouse gas emissions and addressing climate change. Derived from non-fossilized organic materials, biomass emerges as a significant and sustainable contributor to renewable energy. Its diverse nature is complemented by a range of conversion technologies, encompassing combustion, pyrolysis, gasification, and liquefaction, providing versatile avenues for biomass energy transformation. Gasification, the transformative process of converting organic matter into combustible gases under controlled oxygen levels, is accomplished through direct oxygen supply or pyrolysis. This method yields a dependable gaseous fuel versatile for heating, industrial processes, power generation, and liquid fuel production. Machine learning employs advanced statistical techniques for modeling across diverse industries, showcasing particular efficacy in optimizing thermochemical processes by precisely identifying the optimal operating conditions required for achieving desired product properties. These models utilize proximate biomass data to predict the elemental compositions of N2 and H2. Assessment of both single and two hybrid models indicated that the introduced optimizers significantly enhanced the estimation of N2 and H2 when combined with Decision Tree (DT), with Decision Tree Coupled with Artificial Hummingbird Algorithm (DTAH) proving particularly effective. Notably, DTAH demonstrated outstanding performance with remarkable R 2 values of 0.990 for N2 and 0.992 for H2. Additionally, the minimal Root Mean Square Error (RMSE) values of 1.291 and 1.550 for N2 and H2 predictions respectively underscore the precision of DTAH, establishing it as a suitable choice for practical real-world applications.

Investigation of the Sigma Approximation Technique for the Solution of the Time Spectral Equation System

Abstract The time spectral method is commonly utilized to analyze periodic unsteady flows within turbomachines. However, the time spectral solutions in regions with large temporal and spatial gradients, such as wakes, shocks, and boundary layers, can be plagued with unphysical oscillations, known as the Gibbs phenomenon. This paper presents an investigation into the sigma approximation technique, which is capable of significantly attenuating the Gibbs phenomenon by dampening high-frequency nonlinear components in the time spectral solutions. However, the optimal combination of the sigma factors remains to be ascertained considering the trade-off between the accuracy of solutions and the reduction of unphysical oscillations. In this study, a two-row compressor configuration is first selected, and numerical simulations are performed to evaluate the efficacy of this technique under near peak-efficiency and near-stall operating conditions. It is found that Gibbs-type unphysical oscillations can be effectively mitigated without notably compromising solution accuracy through an optimal combination of sigma factors. The NASA Stage 35 case study further verifies the effectiveness of the proposed optimal sigma factor combination in preserving the accuracy of time spectral solutions based on the data from test rig. Moreover, the von Neumann analysis reveals that the sigma approximation technique can enhance the numerical stability of the time spectral equation system, thereby allowing the use of aggressive numerical parameters to accelerate convergence. The stability analysis results are verified by a two-dimension bump under two different flow conditions and further assessed using the two-row compressor.

A position-free control strategy in the full speed domain based on an electro-hydraulic pump

The electric plunger hydraulic pump plays a crucial role in hydraulic transmission technology by integrating an electric motor and a hydraulic pump. The main objectives in designing such devices are to improve efficiency, achieve miniaturization, and promote environmental sustainability. This paper introduces a speed control strategy for an electric hydraulic pump that operates across its entire speed range without relying on position sensor feedback. We begin by establishing the overall model of the electric hydraulic pump and motor characteristics under different operating conditions. Next, we employ a high-frequency injection control method to enable the electric hydraulic pump to start at zero-speed or low-speed states. Additionally, the optimized extended Kalman filtering control method enhances the robustness and tracking performance of the electric hydraulic pump in the medium to high-speed range. Finally, considering the motor characteristics under different operating conditions, we achieve sensorless control of the electric hydraulic pump across the full speed range. Our proposed control strategy provides a promising solution for the control of electric plunger hydraulic pumps, with widespread industrial applications.

Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process.

The bio-pharmaceutical industry heavily relies on mammalian cells for the production of bio-therapeutic proteins. The complexity of implementing and high cost-of-goods of these processes are currently limiting more widespread patient access. This is driving efforts to enhance cell culture productivity and cost reduction. Upstream process intensification (PI), using perfusion approaches in the seed train and/or the main bioreactor, has shown substantial promise to enhance productivity. However, developing optimal process conditions for perfusion-based processes remain challenging due to resource and time constraints. Model-based optimization offers a solution by systematically screening process parameters like temperature, pH, and culture media to find the optimum conditions in silico. To our knowledge, this is the first experimentally validated model to explain the perfusion dynamics under different operating conditions and scales for process optimization. The hybrid model accurately describes Chinese hamster ovary (CHO) cell culture growth dynamics and a neural network model explains the production of mAb, allowing for optimization of media exchange rates. Results from six perfusion runs in Ambr® 250 demonstrated high accuracy, confirming the model's utility. Further, the implementation of dynamic media exchange rate schedule determined through model-based optimization resulted in 50% increase in volumetric productivity. Additionally, two 5 L-scale experiments validated the model's reliable extrapolation capabilities to large bioreactors. This approach could reduce the number of wet lab experiments needed for culture process optimization, offering a promising avenue for improving productivity, cost-of-goods in bio-pharmaceutical manufacturing, in turn improving patient access to pivotal medicine.

Nonlinear flow modeling of electro hydrostatic pump unit based on Gauss Newton iterative method for high performance control

For the electric hydrostatic pump unit (EPU), which is a crucial component in the electro hydraulic servo pump control system (EHSPCS), nonlinear flow modelling is a key challenge to achieve high performance operation, especially in position control. For the nonlinear flow of EPUs, simple linearization combined with compensation has become a popular control method recently. However, the control performance is constrained by the reliance on deviation correction. This paper proposes utilizing the Gauss Newton iterative method to investigate the nonlinear flow modeling in the EPU, aiming to obtain the mapping relationship between the load flow and operating parameters in the EPU, thereby directly improving the control performance. A nonlinear flow model is built by utilizing the Gauss Newton iterative method based on test data obtained from various operating conditions, and the mapping relationship is presented accordingly. Experimental studies show that compared to well-established methods, this model exhibits high accuracy and practicality in improving the control performance, particularly in position control of the EPU.

A Wide-Range TCSC Based ADN in Mountainous Areas Considering Hydropower-Photovoltaic-ESS Complementarity

Due to the radial network structures, small cross-sectional lines, and light loads characteristic of existing AC distribution networks in mountainous areas, the development of active distribution networks (ADNs) in these regions has revealed significant issues with integrating distributed generation (DGs) and consuming renewable energy. Focusing on this issue, this paper proposes a wide-range thyristor-controlled series compensation (TCSC)-based ADN and presents a deep reinforcement learning (DRL)-based optimal operation strategy. This strategy takes into account the complementarity of hydropower, photovoltaic (PV) systems, and energy storage systems (ESSs) to enhance the capacity for consuming renewable energy. In the proposed ADN, a wide-range TCSC connects the sub-networks where PV and hydropower systems are located, with ESSs configured for each renewable energy generation. The designed wide-range TCSC allows for power reversal and improves power delivery efficiency, providing conditions for the optimization operation. The optimal operation issue is formulated as a Markov decision process (MDP) with continuous action space and solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. The optimal objective is to maximize the consumption of renewable energy sources (RESs) and minimize line losses by coordinating the charging/discharging of ESSs with the operation mode of the TCSC. The simulation results demonstrate the effectiveness of the proposed method.

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 [...]

Outdoor Performance Analysis of Semitransparent Photovoltaic Windows with Bifacial Cells and Integrated Blinds

The stricter requirements for the energy performance of buildings are creating a market for several building‐integrated photovoltaic (BIPV) technologies, including photovoltaic (PV) windows. Herein, an innovative multifunctional PV window concept designed to enhance energy generation while providing overheating protection for better indoor thermal and visual comfort is presented. This concept utilizes bifacial c‐Si solar cell strips combined with venetian blinds, all embedded in a unique insulating glazing unit. The bifacial technology increases the energy yield by using the blinds as reflectors, directing more irradiance to the cells’ rear side. The goal of this study is to analyze the outdoor performance of this concept under real operating conditions. Twelve demonstrators are installed and monitored. Various measurement campaigns are conducted, examining the impact of different blind types, tilt angles, sun positions and sky conditions. The highest energy boosts occur when the blinds are fully closed at a 75° angle with their convex side facing outward. Blinds with the highest specular reflectance achieve a maximum performance increase of 25% on sunny days and a daily average increase of 12% compared to the case of no blinds.

Investigation of operational settings, environmental conditions, and faults on the gas turbine performance

Investigation of operational settings, environmental conditions, and faults on the gas turbine performance

Combustion Process of Coal–Açai Seed Mixtures in a Circulating Fluidized Bed Boiler

This study investigates the effects of the co-combustion of coal and açai seed in circulating fluidized bed (CFB) boilers, highlighting the increase in thermal efficiency and relevance of a less-polluting source of energy. Using the computer software 1.5D CeSFaMB™® v4.3.0, simulations of the co-combustion process of coal and biomass were carried out in a CFB boiler, obtaining results such as the temperature profile, boiler efficiency and emissions. The work acquired data regarding the equipment in real operational conditions, consisting of the fundamental geometric and operational parameters used in the simulation campaign. The thermal and chemical properties of the fuels were analyzed by carrying out proximate, ultimate, heating value, particle size and specific mass analyses. The model validation was achieved by simulating the boiler in its real operating conditions and comparing the obtained results with the real data; the obtained error was below 10%. Simulations with different fractions of açai seed for energy replacement (10% and 30%) were carried out. As a result, an increase in the average temperature of the bed was observed, highlighting the region immediately above the dense bed. An increase in boiler efficiency was verified from 56% to 85% with 10% açai and to 83% with 30% açai seed. Decreases in SO2 and CO emissions with the insertion of açai were obtained, showing that co-combustion is more complete, while CO2 emissions were increased due to the higher quantity of fuel inserted into the equipment. The fossil CO2 emissions were reduced.

Catalytic evaluation of eggshell‐based calcium methoxide over Al2O3 for biodiesel generation from waste cooking oil

AbstractShell‐derived materials are promising heterogeneous catalysts for biodiesel production due to their nontoxic and renewable nature, but they have the disadvantage of being highly leachable. To overcome this issue, Ca(OCH3)2 formed through the reaction of CaO with methanol, and supported in turn on Al2O3 is proposed as a catalytic system for the transesterification of fresh soybean oil and waste cooking oil (WCO). First, catalysts with several Al/Ca molar ratios (0.2, 0.5, and 0.8‐AC), as well as their precursors (CaO and Al2O3) alone, were tested at 60 °C, catalyst loading 6% wt, methanol‐to‐oil molar ratio (MOR) 10, for 1 h for fresh oil, and 3 h for WCO. The 0.2‐AC catalyst generated the highest biodiesel yield for both oils. The optimum operating conditions for WCO transesterification, determined by using a univariable approach, were 60 °C, 9 wt% catalyst loading, MOR of 12.5, and a reaction time of 3 h. These improved conditions led to yields higher than 90% for pure CaO and the 0.2, 0.5, and 0.8‐AC catalysts. The density, kinematic viscosity, and refractive index of the biodiesel obtained were measured.

Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi-LSTM

The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi-LSTM. Depthwise separable convolution and Bi-LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat-headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering.

Optimizing Wireless Power Transfer Systems: A Simulation-Based Approach Using CST Studio Suite

In the rapidly evolving field of wireless power transfer (WPT), achieving systems that combine efficiency with reliability is crucial. This paper presents a simulation-based approach using CST Studio Suite to optimize WPT systems for wireless battery chargers in electric bikes. The study fixes the size of both the transmitter and receiver coils, the spacing between turns, and the number of coil turns, with a set distance of 20mm between the transmitter and receiver coils. CST Studio Suite is employed to determine the parameters of resonance capacitors and the optimal switching frequency to enhance WPT efficiency. This powerful simulation tool allows researchers and engineers to design resonance compensation circuits more effectively, optimizing power transfer efficiency. The simulation conducted with CST Studio Suite confirms its effectiveness in designing a WPT system, achieving an impressive peak efficiency of 99.61% at the resonant frequency. The paper demonstrates CST Studio Suite's capability to accurately predict WPT system performance by developing detailed electromagnetic models and fine-tuning simulation parameters to reflect real operating conditions. Iterative simulations resulted in optimized practices, validated by select results that highlight the method's effectiveness and showcase an optimal balance between system complexity and efficiency. These findings provide a strategic framework for future WPT system development, offering practical guidance for researchers and industry professionals aiming to advance WPT technology.

Optimization of aero-engine condition monitoring by autoregressive model analysis based on wireless communication

The high value of aero-engines makes it necessary to extend their life in terms of econ-omy of use. Health management is an important basis for this work. Background: The vibration signal is an important representation of the working state of an aero-engine. The current usual method is based on wired communication with simple waveform presentation. The many incon-veniences of this method have seriously affected the further development of this work. Methods: This research team acquires engine vibration signals in real time through a designed wireless signal sensor, and then analyzes and visualizes the signals in depth. Results: Through this method, it can visually and effectively demonstrate signal characteristics for engineers, effectively identify fault signals, and monitor engine operating conditions in real time. Conclusions: The method enables the optimization of aero-engine work-state monitoring.

Impact of variable hydrostatic pressure and intermittent operation on filtration performance and biofouling layer in gravity-driven membrane system for practical decentralized water supply

The gravity-driven membrane (GDM) systems offer a promising solution for decentralized drinking water supply due to its low maintenance and energy consumption. However, research on the practical application of GDM under variable hydrostatic pressure (variable load) and intermittent operation conditions, particularly in response to high-turbidity water, remains limited. This study investigates the long-term performance and characteristics of the biofouling layer of GDM under ultra-low variable hydrostatic pressure (20–60 mbar) and intermittent operation (8–12 h) conditions in practical decentralized water supply. The results indicate that the membrane flux (1.9–6.0 L∙m−2∙h−1, LMH) remained relatively stable despite variations in gravity-driven pressure (ΔP). Long-term operation of GDM can stably remove turbidity and potential pathogens from raw water. The total protein/polysaccharide ratios of extracellular polymeric substances (EPS) in the biofouling layer were below 0.50, reducing contaminant adhesion on the membrane surface. The 0.2–0.4 μm transparent exopolymer particles (TEP) fraction might be crucial for biofouling layer formation. The key species, belong to Proteobacteria and Patescibacteria, significantly alleviated membrane fouling by degrading polysaccharide and proteins in biofouling layer. Comammox Nitrospira were significantly enriched, contributing to efficient nitrification. GDM with variable load maintained a stable flux of 2.2–5.2 LMH under high-turbidity water conditions (300–1200 NTU), and simple forward flushing combined with sludge discharge can quickly restore ΔP. Overall, the variable load GDM system effectively manages membrane fouling and maintains stable filtration performance through the combined effects of variable load and intermittent operation, ensuring long-term and low-maintenance operation for decentralized water supply.

Flexible curved multi-functional sensors for air flow and vibration signal measurement of train bogie

Abstract The fluid-solid coupling effects with the aerodynamic noise and structural vibration signal have great part in measurement accuracy and fault diagnosis of the train bogie. It is important to collect aerodynamic noise and structural vibration data for safety evaluation and on-line structural health monitoring (SHM) of the bogie, which brings new challenges to the integration and installation of the sensors in the limited space and complicated operation condition of the train bogie. Flexible curved multifunctional sensors (FCMFS) integrated with the hot film and structural vibration sensor units is designed to the air flow and structural vibration measurement of the train bogie. Bogie vibration and wind tunnel test platforms with FCMFS are built to the air flow velocity and vibration data measurement on the bogie surface under different operation conditions. Piezoelectric Polyvinylidene fluoride(PVDF) sensors have significant output in the wind tunnel and vibration experiments, and the hot film sensor is only sensitive to the air flow signal. The effect of vibration signal to the hot film sensor could be ignored, which is applied to decouple the aerodynamic noise and structural vibration signals for improving the measurement accuracy of the train bogie. The dynamic performance of FCMFS is verified in Rail Transmit Base of East China Jiaotong University, which is applied to SHM and rapid fault diagnosis of train bogie. The air flow distribution and vibration data are collected to safety assessment and fault diagnosis of train bogie, which would promote the intelligent maintenance level of the train bogie.

From single to multi‐purpose reservoir: A framework for optimizing reservoir efficiency

AbstractThe prevailing challenge centers on the limited application of single‐purpose flood control reservoirs transitioning to multi‐purpose reservoirs, which, despite their potential, often fall short in addressing the escalating demands for diverse water resource management. These challenges are compounded by outdated operational rules and changing environmental conditions. This research develops a framework to enhance the dual functionality of reservoirs, initially designed for flood control, to also support water supply through the determination of maximum safe water levels (MSWLs). Utilizing historical inflow data and reservoir simulation models, the study identifies opportunities for optimizing United States Army Corps of Engineers Louisville District reservoirs. It highlights certain reservoirs as ideal for augmenting water supply capabilities without compromising flood control performance. Others remain critical for flood management due to limited water supply potential, underscoring the importance of maintaining a focus on flood control. The findings illuminate the intricate balance required between managing flood risks and enhancing water supply, indicating that precise operational adjustments can significantly improve reservoir sustainability and efficiency. This method offers a viable pathway to convert single purpose reservoirs into multi‐purpose reservoirs, meeting growing water demands while ensuring robust flood mitigation, and making a step toward better water utilization.

Fluorobenzene as new working fluid for high-temperature heat pumps and organic Rankine cycles: Energy analysis and thermal stability test

Industrial high-temperature heat pumps and Organic Rankine Cycles play a pivotal role in reducing CO2 emissions of the industrial sector. While several eco-friendly refrigerants have been explored for subcritical heat pumps below 150 °C, above this threshold only a few fluids can be adopted.In this article, fluorobenzene (C6H5F) is proposed for the first time as a versatile working fluid suitable for both HTHP and ORC systems. Notably, it possesses a near-zero Global Warming Potential, null Ozone Depletion Potential, low cost, and low toxicity. The thermo-chemical stability of fluorobenzene is experimentally investigated with an advanced procedure, simulating the presence of the non-condensable-gases removal system in real plant operating conditions. The yearly rate of unimolecular decomposition is estimated less than 4 % at 350 °C, and even after 400 h of thermal stress no decomposition products have been detected in the liquid phase through Fourier Transform Infrared Spectroscopy.In a direct heat exchange case study, coupled with exhaust gases at 390 °C, fluorobenzene achieves a net power production higher than other commercial fluids adopted in high-temperature units. In subcritical two-stage throttling heat pump condensing at 180 °C fluorobenzene shows a good Coefficient of Performance of 3.25 at 100 °C temperature lift.

Prediction of particulate matter during the combustion of wood pellets with the addition of face mask waste

The amount of plastic waste has increased enormously due to the using of protective equipment in order to slow down the viral transmission of COVID-19. The usage of protective face masks brought a huge environmental burden. Moreover, a lot of disposed masks enter the ocean or end on landfills, where they threaten the ecosystem. It is still necessary to deal with environmental-friendly disposal or to reuse and recycle of these masks. This article is focused on the disposal of FFP2 face masks by their co-combustion with wood. Due to the possibility of produced emissions, disintegrated masks were blended with spruce and beech sawdust and compared with pure wood. These materials were compressed into pellets with the aim of higher density. The emissions such as carbon monoxide, nitrogen oxides, sulphur oxides and particulate matter were measured during pellet combustion. The heat output of the automatic pellet boiler was also determined. The filters with captured particulate matter were sent to the investigation of dioxin and furan concentration. Except for this, the regression model has been created for the prediction the concentration of particulate matter. The results confirmed that the co-combustion of wood with FFP2 masks could be one of the environmental-friendly ways of face mask disposal when they are used as an additive in a small percentage. The fuel composition and operational conditions are also very important parameters during the combustion process. However, the concentration of gas emissions and particulate matter, as well as heat output, did not change significantly, when the content of FFP2 masks of 5 % or 10 % was used.

Physical properties and microstructural corrosion propertiesof Laser-bent 304 Stainless Steel Sheets

Laser treatment by laser bending is a technique of treating sheet metal by thermal residual stresses generated by laser assisted heating without any externally applied mechanical forces. The advantages of laser assisted bending over conventional bending include flexibility of non-contact processing, amenability to materials with diverse shape/geometry, and high precision/productivity. Up to now, most of previous work on laser bending was concentrated on study of bending angles/shapes with laser operating conditions, and modelling of the thermal process, however, no work has been reported on corrosion behavior of the laser-bended components. Since the laser bending is a thermal process, it is believed that the thermal process could not only alter microstructure, but also affect corrosion performances, such as intergranular corrosion, due to possible sensitisation caused by repeating laser scans. The aim of the present work is to investigate corrosion performance of laser-bent 304 austenitic-steel sheet with thicknesses of 2 mm and 1.5 mm, respectively, using a 2 kW CO2 laser. After laser treatment, the laser-bent samples were characterised in terms of microstructural change within bent zones consisting of melt-zone and heat-affected zone, using optical microscopy and scanning electron microscopy (SEM). Hardness profiles were obtained across the bent zones. Corrosion performances of the laser-bent evaluated by means of electrolytic etching in oxalic acid and double-loop electrochemical potentiokinetic reactivation (DL-EPR) tests. Laser bending is a thermal process, it is believed that the thermal process could not only alter microstructure, but also affect corrosion performances, due to possible sensitisation caused by repeating laser scans.