Optimal Operating Parameters Research Articles

Performance improvement with boosted intake pressure and hydrogen enriched biogas and producer gas-fuelled SI engine

The depletion of conventional fuels and rising energy demand necessitate alternative energy solutions, with biomass emerging as a viable resource. However, relying on a single renewable source poses challenges like inconsistent supply and reduced engine power output. This study numerically simulates a spark-ignition (SI) engine fueled by a triple fuel blend of producer gas (PG), biogas (BG), and hydrogen (HG). A comprehensive quasi-dimensional thermodynamic model, coded in FORTRAN, simulates SI engine performance by varying input factors such as biogas blending ratio, intake pressure, and late inlet valve closure timing (LIVC). This study introduces a novel approach to simulate the combined impact of triple fuel blending concentration, intake boost pressure, and inlet valve closure timing on SI engine performance and emissions. Additionally, it aims to identify optimal operating parameters to maximize power and efficiency while minimizing fuel consumption and emissions. Using response surface methodology and an analysis of variance regression model, results showed statistical significance at a 95 % confidence level. Optimal conditions were found at 82.99° aBDC for LIVC timing, 23.28 % biogas concentration, and 1.99 bar inlet pressure. Under these conditions, the engine achieved 2.39 kW brake power, 28.92 % brake thermal efficiency, 12,461 kJ/kWh brake-specific energy consumption, 0.081 % carbon monoxide, and 1640.36 ppm nitric oxide, with a composite desirability of 0.843. In triple fuel blend, while biogas concentration changes from 40 % to 23.28 % with increasing the intake pressure from 1 to 2 bar, BTE improves from 22.24 to 28.92 %. These results demonstrate that simulation and optimization can effectively predict SI engine performance with blended gaseous fuels.

Prediction and Control of Existing High-Speed Railway Tunnel Deformation Induced by Shield Undercrossing Based on BO-XGboost

The settlement of existing high-speed railway tunnels due to adjacent excavations is a complex phenomenon influenced by multiple factors, making accurate estimation challenging. To address this issue, a prediction model combining extreme gradient boosting (XGBoost) with Bayesian optimization (BO), namely BO-XGBoost, was developed. Its predictive performance was evaluated against conventional models, such as artificial neural networks (ANNs), support vector machines (SVMs), and vanilla XGBoost. The BO-XGBoost model showed superior results, with evaluation metrics of MAE = 0.331, RMSE = 0.595, and R2 = 0.997. In addition, the BO-XGBoost model enhanced interpretability through an accessible analysis of feature importance, identifying volume loss as the most critical factor affecting settlement predictions. Using the prediction model and a particle swarm optimization (PSO) algorithm, a hybrid framework was established to adjust the operational parameters of a shield tunneling machine in the Changsha Metro Line 3 project. This framework facilitates the timely optimization of operational parameters and the implementation of protective measures to mitigate excessive settlement. With this framework’s assistance, the maximum settlements of the existing tunnel in all typical sections were strictly controlled within safety criteria. As a result, the corresponding environmental impact was minimized and resource management was optimized, ensuring construction safety, operational efficiency, and long-term sustainability.

Optimization of Process Design and Operating Parameters of H2S Removal Unit to Reduce Lean Amine Inlet Temperature of Amine Contactor at Upstream Oil and Gas Subsidiary SI

Upstream Oil and Gas Subsidiary SI is a natural gas processing company that operates an H2S removal unit to convert natural gas rich in CO2 and H2S into sweet gas. The main problem of this unit is the high temperature of lean amine entering the Amine Contactor. The purpose of this study is to determine the factors of high lean amine temperatures, evaluate the lean amine cooler, amine regenerator overhead cooler, and plate exchanger performance, and determine the optimal process design configuration and operating parameters. The method used is the simulation of the H2S removal unit with Aspen HYSYS, followed by a comparative analysis between simulation data and equipment design data. The independent variables of this study include Reboiler duty, reflux ratio, and heat transfer area in the Plate Exchanger, with the main dependent variable being lean amine temperature. The results showed that the high lean amine temperature was caused by a decrease in the performance of the Lean Amine cooler and amine regenerator overhead cooler, as seen from the UA and LMTD values of the simulation results which were smaller than the design. In contrast, the Plate Exchanger still functions well with a UA value greater than the design. Optimization was carried out by adjusting the process design and operating parameters of the H2S removal unit. The optimized design involves bypass reflux from the regenerator to the rich amine stream entering the rich/lean amine exchanger and increasing the heat transfer surface area of the plate exchanger to 62.17 m². The influential operating parameters are reboiler duty, liquid flow rate to the mixer, and plate exchanger heat transfer surface area. Optimal operating conditions were achieved at a Reboiler duty of 1,642 kW, a liquid flow rate of 1.4 m³/h, the reflux to Mixer ratio is 100%, and a heat transfer surface area of 62.17 m². In addition, it can be concluded that the optimization of process design and operating parameters successfully reduced the lean amine inlet temperature of the Amine Contactor.

Removal of Copper Ions from Aqueous Solutions Using a Nanomembrane Ceramic Filter Doped with Magnetic Zeolite

This study investigates the removal of copper ions from aqueous solutions using a nanomembrane ceramic filter doped with magnetic zeolite. The filter was designed using indigenous materials quartz, clay, and CaCO3 each selected for its unique functional properties. Characterization via Scanning Electron Microscopy revealed a well-distributed porous structure that enhances the filter's adsorption capacity. XRD patterns confirmed the crystalline nature of the ceramic filter and the successful incorporation of magnetic nanoparticles into the membrane matrix. VSM analysis showed that the magnetic properties of the zeolite were superparamagnetic, with a maximum saturation magnetization of 0.08emu/g. Additionally, zeta potential measurements indicated a strong negative charge of -60.9mV on the material. The filter’s copper removal efficiency was tested under various conditions (pH, initial concentration, and contact time), achieving a maximum removal efficiency of 99% at pH 9 and an initial concentration of 60mg/L. Optimization of operational parameters using Response Surface Methodology (RSM) with a Box–Behnken design resulted in a satisfactory coefficient of determination (R² = 0.80), indicating a good fit between experimental data and predicted results. Overall, the comprehensive characterization and optimization results highlight the potential of nanomembranes filter as an effective and economical solution for copper removal water.

Study Explores Interaction Between Bit Whirl and High-Frequency Torsional Oscillations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23989, “The Nature of the Interaction Between Bit Whirl and High-Frequency Torsional Oscillations,” by Andreas Hohl, SPE, and Armin Kueck, Baker Hughes, and Vincent Kulke, TU Braunschweig, et al. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference. _ Understanding the interaction of high-frequency torsional oscillations (HFTO) with other vibrational phenomena is essential to develop effective HFTO mitigation strategies. While coupling with axial vibrations and stick/slip already have been studied extensively, the interaction of HFTO with lateral vibrations has received less attention. The complete paper analyzes this interaction based on a bit/rock interaction model that accounts for the superimposed movement of whirl and HFTO at the bit. The new bit model provides a physics-based explanation of why bit backward whirl and torsional vibrations cannot be observed simultaneously. Introduction The torsional movement of HFTO is localized to the lower part of the bottomhole assembly (BHA). The amplitudes along the distance from the bit are scaled by complex mode shapes with a comparably small wavelength. This deflection of the BHA leads to high tangential acceleration and dynamic torsional torque loads. The self-excitation mechanism of HFTO can be modeled as a drilling torque that decreases when the bit rotary speed increases. The nonlinear drilling torque characteristic can be identified from field data. Because both stick/slip and HFTO correspond to a torsional movement of the drilling system, and both are excited by the rock-cutting process, they interact strongly. The mitigation strategies and optimal operational parameters to manage HFTO are, thus, strongly linked to the understanding of this interaction. In this work, the interaction of HFTO and bit backward whirl was analyzed and modeled to further interpret this observation. Bit backward whirl is a complex lateral movement of the bit. The understanding of the interaction between HFTO and backward whirl drives improvements of vibration-mitigation strategies and bit design to mitigate HFTO or bit backward whirl. Modeling of the Cutting Forces To study the interaction of whirl and HFTO in more detail, a simplified bit cutting-force model is needed. For a common polycrystalline-diamond-compact (PDC) bit, the cutting torque of the bit results from the rock-cutting process. The cutting process results in cutting forces on each individual cutter. For simpler modeling, the cutting forces are assumed to be proportional to the normal force on each cutter. The proportionality factor between the normal force and the cutting force at a cutter is called cutter aggressiveness and is dependent on the velocity of the cutter. The radial distance between the geometric center of the bit and a cutter, multiplied by the cutting force, results in the portion of the cutting torques of each cutter. These add up to the overall bit torque. The three central variables in the torque calculations are the normal force, cutting speed, and cutter aggressiveness.

Biosynthesis and Application of Biological Thin Films for Heavy Metal Ion Biosorption from Aqueous Solution

This study investigates the biosynthesis and application of Mycelium Bio-Films (MBFs) derived from pure Common oyster mushroom (COM) for heavy metal ion biosorption from aqueous solutions. The research focuses on the growth mechanism of MBFs and their potential for Cu (II) ions removal. MBFs were synthesized through a hyphae cultivation process and characterized using Scanning Electron Microscopy (SEM), Fourier-Transform Infrared Spectroscopy (FTIR), X-ray diffraction (XRD), and Energy-Dispersive X-ray Spectroscopy (EDS) mapping. Batch biosorption experiments were conducted to determine optimal operational parameters and adsorption mechanisms. Key findings reveal optimal biosorption conditions at pH 5, with a 40-minute contact time, and 100mg/L initial Cu (II) concentration using 0.2g of biosorbent in 10mL aqueous solution at room temperature with 150rpm agitation. Under these conditions, a maximum adsorption efficiency of 82.8% was achieved. The adsorption process best fits Pseudo-second-order kinetics and Langmuir isotherm, indicating chemisorption, complexation, and ion exchange as primary mechanisms. Functional groups involved in biosorption include carboxyl, hydroxyl, and amide groups. Importantly, MBFs demonstrate high reusability potential through diluted acid elution. This research contributes to environmental remediation by presenting a novel, sustainable biosorbent for heavy metal removal. The MBFs show promise for industrial wastewater treatment applications, offering an eco-friendly alternative to conventional adsorbents and contributing to filling the lack of biobank collections. Future studies could explore the efficacy of MBFs with other heavy metals and investigate potential scale-up for large-scale implementation.

Dual-stage vacuum pressure swing adsorption for green hydrogen recovery from natural gas grids

Purification of green hydrogen (GH) from natural gas grids (NGG) can be expensive and challenging through a single-step Pressure Swing Adsorption (PSA) process due to the low H2 concentration in the grid (<20 % v/v). Herein, we report for the first time the design of a dual-stage vacuum pressure swing adsorption process (DS-VPSA) to purify H2 blended in NGG with a synergy action of two types of adsorbents: a Carbon Molecular Sieve 3K (CMS) in stage 1 and zeolite 13X in stage 2. In Stage 1, the CMS kinetically separates H2 from CH4, pre-concentrating H2 from 20 % to over 50–60 % (v/v), followed by Stage 2, where a thermodynamic separation with zeolite 13X achieves a final product with a high H2 purity content (>99 % v/v). A mathematical model is developed in Aspen adsorption, where numerical simulations are performed to establish the best operating conditions of the global DS-VPSA. A parametric study is also conducted to optimize performance parameters such as recovery, purity, productivity, and specific energy. The results indicate that it is possible to achieve a final H2 product with a purity of 99.97 % (fuel cell grade), a recovery of 67 %, and productivity of 1.60x10-2 kgH2/kgads/hr, and a total specific energy consumption of 10.06 MJ/kgH2, which is a significant achievement reported so far.

Synergistic mechanisms of magnesium slag coupled with dust removal ash for CO2 sequestration via direct aqueous carbonation: High mineralization efficiency and optimization of reaction parameters

Utilizing natural industrial solid waste for CO2 capture and utilization can reduce both the environmental problems caused by industrial solid waste and greenhouse gases. Based on this idea, magnesium slag-based solid waste has great economic and environmental value as CO2 storage and backfill material. In this study, the CO2 mineralization capacity and efficiency of two magnesium slag-based industrial solid wastes including magnesium slag (MS) and dust removal ash (DRA) and their mixtures were studied by gas-liquid two-phase mineralization method. The as-prepared samples were characterized by a series of techniques such as XRF, XRD, SEM, FT-IR and TG tests. The results exhibited that compared with single MS and DRA sample, the mineralization efficiency of composites increased 7.63 % and 14.1 %, respectively, which performed a synergistic effect. In addition, the main operation parameters including reaction temperature, solid-to-liquid ratio and CO2 concentration were investigated, and the temperature of 35 ℃, solid-to-liquid ratio of 0.1 g/mL and CO2 concentration of 30 vol% were chosen as optimum operating parameters. Furthermore, the reaction mechanism of CaCO3 formation and the mineralization efficiency improvement after cooperation were expounded.

Recovery of elemental arsenic from calcium arsenate slag by carbothermic reduction: Ca3(AsO4)2 → Ca3(AsO3)2 → As4(g) pathway

With the increasing application of As(0) in semiconductor, equipment manufacturing fields, recovery of As(0) from hazardous wastes-bearing arsenic has attracted great attention. As alkali leaching method is widely used for disposal of wastes-bearing arsenic, secondary arsenic pollution has become an inevitable environmental problem due to the generated calcium arsenate slag. To solve secondary arsenic contamination, a reduction roasting process was proposed to recover As(0) from calcium arsenate slag. Thermodynamic calculation suggested that reduction pathway Ca3(AsO4)2 → As4(g) is the most feasible, followed by As(s) and As(g). Increasing temperature is conducive to reduction of Ca3(AsO4)2(s) to As4(g). To achieve efficient reduction of Ca3(AsO4)2 and avoid generation of As4O6(g), the temperature should be 650 °C ∼ 952.88 °C and molar ratio of C/Ca3(AsO4)2 should be greater than 3.0. The optimum operating parameters were determined to be C dosage of 14 %, temperature of 900 °C, and reduction time of 2 h, arsenic recovery efficiency and purity of As(0) reached 99.22 % and 99.41 %, respectively. Besides, mechanism of reduction process was elucidated and the findings revealed that reduction process is carried out in steps by Ca3(AsO4)2 → Ca3(AsO3)2 → As4(g) pathway. Moreover, this method has been successfully applied for semi-industrial test, providing an alternative solution for treatment of arsenic-bearing wastes.

Rapid Treatment of Urban Initial Rainwater Runoff by A/O–Magnetic Flocculation Combined Process

The acceleration of urbanization with the increased proportion of impermeable surfaces has posed significant challenges for old urban drainage systems, particularly during periods of heavy rainfall. Urban initial rainwater runoff pollution, containing a considerable quantity of pollutants, has severely contaminated the urban water environment. The present study presented an A/O–magnetic flocculation (A/O-MF) process for the rapid treatment of urban initial rainwater runoff pollution to realize the simultaneous removal of NH4+-N, TN, COD, TP, and SSs. The optimal operation parameters were obtained by the single-factor and orthogonal methods. The results showed that the optimal operation duration of the A/O process was 10 min for the anoxic process and 60 min for the aerobic process. The optimal dosages of the flocculants were 105 mg/L for PAC, 3 mg/L for PAM, and 30 mg/L for Fe3O4 with a sedimentation time of 2 min. To treat the actual runoff rainwater, the A/O-MF process improved the removal efficiencies of TP, SSs, NH4+-N, and COD compared with the single A/O process, with efficiencies of 99.5%, 93.3%, 99.7%, and 91.3%, respectively. The total operation duration was only 74 min, which could enable the rapid and efficient treatment of urban runoff rainwater.

Experimental treatment of pickled vegetable wastewater by electrofenton method based on modified PbO2 electrode

The pickle industry is acknowledged as one of the most environmentally harmful sectors within the food industry. The elevated concentrations of chemical oxygen demand (COD), total phosphorus (TP), ammonium nitrogen (NH4+-N), and salinity found in pickle wastewater present significant environmental challenges. To tackle these issues, this study investigates the efficacy of a La-doped Ti/SnO2-Sb2O5/La-PbO2 electrode employing an electro-Fenton method for the treatment of pickle wastewater. Experimental design was performed using response surface methodology to identify optimal operational parameters. Results demonstrate that all processes adhere to pseudo-first-order reaction kinetics. Under conditions where the degradation temperature is maintained at 65 °C, current density at 100 mA cm−2, pH at 4.1, FeSO4 concentration at 3.03 mmol L−1, and H2O2 concentration at 2.07 mmol L−1 with six additions over a degradation period of 240 minutes, COD removal achieved an impressive rate of 92.87 %, while ammonium nitrogen removal reached a remarkable rate of 95.26 %. In comparison to traditional electrochemical methods, both COD and ammonium nitrogen removal rates exhibited substantial improvements; furthermore, hydroxyl radicals (•OH) played a pivotal role in facilitating the radical quenching degradation process. The operation cost analysis shows that the electric Fenton process has a lower operation cost, which provides a good opportunity for the food industry to treat Pickled vegetables wastewater. The operating cost for COD removal rate is 1.262 $ m−3kg−1, and the operating cost for ammonia nitrogen removal rate is 1.355 $ m−3kg−1.

4E evaluation and multi-objective optimization of low concentration flue gas CO2 capture and multi-product conversion process

In order to address the current global warming, this work proposes CO2 capture from flue gas and CO2 hydrogenation to produce methane, methanol, and CO2 to produce ammonium bicarbonate. Aspen Plus process simulation model is developed for the three processes. Based on the thermodynamic model, sensitivity analysis of main operating parameters is conducted on the energy, economic, environment, and exergy (4E) indicator. Non-dominated sorting genetic algorithm is used to achieve multi-objective optimization. The optimum operating parameters of the three processes are obtained under the 4E indicators. It is concluded that the CO2 to ammonium bicarbonate is the optimal process route for CO2 conversion. The corresponding total annual cost is 4.071 M$/year, the CO2 equivalent emission per unit product is −0.0667 tCO2/t pro, the annual total utility consumption is 7062.75 kW/year, and the exergy efficiency of system is 53.42 %. This work shows significance for achieving resource utilization with capture CO2.

Novel methodology for targeting the optimal reactor and operating parameters based on the chemical system’s overall performance within catalyst lifecycle

Catalyst deactivation affects chemical system performance and reactor operation. A systematic method is proposed for targeting the optimal reactor, operating parameters, system performance, and catalyst service life, considering the catalyst deactivation. Relations between reactor performance, operating parameters, and running time are clarified based on the coupling analysis of the reactions, catalyst deactivation kinetics, and mass/energy balance. The influence of reactor fluctuation on energy cost and product output is explored by topological analysis, pinch analysis, and algebraic reasoning. A reactor-separator-heat exchanger network coupling frame is established to predict system performance and guide the reactor selection, catalyst regeneration, and system adjustments. The proposed method is intuitive and efficient and can be applied in the preparatory/operation stage. For the studied benzene hydrogenation process, the Plug Flow Reactor is suitable; the catalyst’s optimal service life is 2.08 y, achieving 4.4 % and 4.8 % decreases in annual cost and energy demand/carbon emission by real-time adjustments.

Analysis and Optimization of Cryogenic Distillation Systems: For Reducing Distillation Energy Consumption

AbstractCarbon capture utilization and storage‐enhanced oil recovery (EOR) is often considered the most promising technology for utilizing CO2. Cryogenic distillation is also viewed as the most reasonable separation option for handling CO2‐EOR gases, despite being an energy‐intensive process. The main challenge for this technology is energy loss. To overcome this challenge, one potential alternative is to optimize the system processes and parameters. This study proposes a new process to reduce distillation energy consumption by refluxing the distillate back to the distillation column. Operational parameter optimization was performed using the commercial simulator Aspen HYSYS for modeling and sensitivity analysis of process parameters using orthogonal experimental methods. The simulation results indicate that after optimization, the energy consumption in the distillation process decreased from 0.207 to 0.196 MJ kg−1, whereas the purity decreased slightly from 94.63 % to 94.52 %. However, the recovery increased from 97.8 % to 97.88 %, and the total energy consumption decreased from 0.772 to 0.761 MJ kg−1.

Towards a Sustainable Blockchain: A Peer-to-Peer Federated Learning based Approach

In the rapidly evolving digital world, blockchain technology is becoming the foundation for numerous applications, ranging from financial services to supply chain management. As the usage of blockchain is becoming more prevalent, the energy-intensive nature of this technology has raised concerns about its long-term sustainability and environmental footprint. To address this challenge, we explore the potential of Peer-to-Peer Federated Learning (P2P-FL), a distributed machine learning approach that allows multiple nodes to collaborate without sharing raw data. We present a novel integration of P2P-FL with blockchain technology, aimed at enhancing the sustainability and efficiency of blockchain networks. The basic idea of our approach is the use of distributed learning mechanisms to find the optimal performance parameters of blockchain without relying on centralized control. These parameters are then used by a load-balancing mechanism that prioritizes energy efficiency to distribute loads on different blockchains. Furthermore, we formulate a non-cooperative game theory model to align the individual node strategies with the collective objective of energy optimization, ensuring a balance between self-interest and overall network performance. Our work is exemplified through a case study in the renewable energy sector, demonstrating the application of our model in creating an efficient marketplace for energy trading. The experimentation and results indicate a significant improvement in the execution times and energy consumption of blockchain networks. Therefore, the overall sustainability of the network is enhanced, making our framework practical and applicable in real-world scenarios.

Simulation optimization and experimental validation of hydraulic impact in pruning machines

Abstract This study addresses the instability caused by hydraulic shocks in fruit tree pruning machines during operation and proposes an optimization scheme that combines simulation with experimentation. We investigated the mechanisms of load shock in the hydraulic system through mathematical modeling and LS-DYNA simulation analysis. The optimal operating parameters were determined using mechanical and orthogonal analyses: a forward speed of 4 km h−1, a pruning tool angle of 60°, and a saw blade rotation speed of 1200 r min−1. Additionally, we employed AMESim simulation to optimize the hydraulic system of the pruning machine by designing a dual accumulator configuration to effectively suppress hydraulic shocks. We validated the optimized system through simulations and experimental tests, demonstrating significant reductions in pressure fluctuations, with cutting efficiency improved by 30% and pressure fluctuations reduced by 26.7%. This study not only presents an innovative dual accumulator design but also validates its effectiveness in pruning machines through simulations and experiments, thereby enhancing the operational efficiency and stability of agricultural machinery.

Quasi-1D numerical analysis and Bayesian optimization of two-stage light gas gun operational parameters

Quasi-1D numerical analysis and Bayesian optimization of two-stage light gas gun operational parameters

Dynamic liquid level prediction for multiple oil wells based on transfer learning and multidimensional feature fusion network

Abstract Accurate prediction of the dynamic liquid level (DLL) in oil wells is crucial for the intelligent optimization of pumping systems. It provides real-time insights into the operational conditions of the pumping system but also supports the optimization of operational parameters with data. However, due to the long-term operation of oil wells and their complex internal environments, direct measurement of the DLL is challenging, leading to low reliability of the obtained data. Therefore, this paper conducts an in-depth analysis of the parameters involved in the pumping process, identifies the model’s input features, and develops a DLL prediction model for multiple wells based on multidimensional feature fusion (MFF). This model captures the characteristics of DLL changes and the diversity of input features. To address the issues of slow model training and low prediction accuracy caused by insufficient datasets in practical applications, this paper integrates transfer learning (TL) techniques. It proposes a new model, the DLL model for multiple wells based on TL and multidimensional feature fusion (TMFF). Initially, the Euclidean distance and maximum mean discrepancy methods are employed to verify the feature similarity between the source and target domains, using highly similar DLL data as experimental data. By combining TL techniques with the MFF model, the TMFF model is established. The model’s capabilities are validated using field-collected data with broad representativeness. Experimental results demonstrate that the proposed MFF model possesses high accuracy and generalization capability. Additionally, the TMFF model effectively resolves the issue of insufficient data during model training. In summary, the methods proposed in this paper can provide accurate DLL data for practical applications in intelligent oilfields.

Development of high-speed precision maize metering device for dense planting pattern with standard ridges.

This study proposed a high-speed precision dual-chamber maize metering device for the dense planting pattern with standard ridge promoted in China. Through theoretical analysis of the sowing process, parameters for the key components have been designed. The metering device is capable of planting two rows in a single pass for high-speed precision seeding. The effect of operating speed and negative pressure on seed metering quality was investigated. A high-speed camera was used to capture the trajectory of maize seed at different operating speeds, and it was found that intra-row shifts were caused by collisions at the mouth of the seed guide tube and rotation of the seed as it fell. Employing a two-factor, five-level orthogonal rotation test, Investigated the optimal operating parameters for the maize metering device. Response surface analysis showed that optimum seed metering quality was achieved at 14.2 km/h and 13.5 kPa. Validation tests showed a qualified rate of 97.93%, with a coefficient variation of 8.97% for this method. Additionally, an energy consumption analysis indicated a reduction in operating energy consumption of approximately 32% compared to conventional air suction seed metering devices for dense planting with large ridges on the same area of farmland. This study provides insights for reducing energy consumption in the seeding process, contributing to the sustainable development of agricultural resources.

The Impact of Non‐Monolithic Semiconductor Capacitance on Organic Electrochemical Transistors Performance and Design

AbstractThe existing device models for organic electrochemical transistors (OECTs) fail to provide any device design guidelines for optimized performance parameters such as transconductance that are pivotal for the applications OECTs in sensing. Moreover, the current models are based on the questionable assumption of a homogenous organic semiconductor layer, and all predict a linear behavior of the resistance with the OECT channel length. Consequently, the experimentally observed nonlinear resistance behavior in OECTs has been overlooked thus far. Here, an OECT device model is developed that accurately describes the nonlinear behavior of the OECT channel resistance and offers the first guidelines for maximizing transconductance. The model is inherently nonlinear and the nonlinearity stem from the non‐monolithic capacitance of the organic semiconductor layer. Moreover, the model provides a consistent and reliable estimations for the contact resistance in OECTs. The success of the model in accurately describing and providing predictions of the OECT operation by relating the device's geometrical parameters with electrochemical parameters of the semiconductor layer paves the way toward unlocking OECT potentials in diverse applications, from biosensing to neuromorphic computing and flexible electronics.