Power Generation Research Articles

Synergistic mechanisms of ethanol and butanol in gasohol blends in 4-stroke SI engines for green sustainable energy solutions: revolutionizing engine efficiency, power output and emission reduction for net-zero transportation systems

Abstract This study systematically investigates ethanol, butanol, and gasohol blends in a 4-stroke spark-ignition (SI) engine to explore sustainable and efficient fuel alternatives. The research focuses on understanding the interactions between engine performance and emissions while highlighting the combined effects of ethanol and butanol in gasohol formulations. The research methodology involves a comprehensive investigation into the intricate relationship between power generation, emissions, and efficiency by testing fuel blends with varying ethanol and butanol proportions (5 %, 8 %, 10 %, 12 %, and 15 % volume fractions). A range of performance parameters – including Brake Power (BP), Brake Specific Fuel Consumption (BSFC), Mechanical Efficiency (ME), and Exhaust Gas Temperature (EGT) – are assessed using a state-of-the-art experimental setup. Additionally, the study evaluates the environmental impact of each fuel blend by analyzing emissions of CO, CO2, NOx, and HC. The comparative analysis identifies optimal ethanol-butanol ratios that enhance engine performance while reducing emissions. The results indicate that increasing the alcohol content improves power output and mechanical efficiency while lowering carbon emissions. Specifically, the ethanol-butanol blend with a 12 % volume fraction demonstrated a 6.8 % increase in Brake Power and a 9.5 % reduction in CO emissions compared to conventional gasohol. However, higher ethanol content, due to its greater latent heat of vaporization, led to a 4.2 % decrease in NOx emissions but also a slight increase in BSFC by 3.1 %, highlighting the trade-offs in combustion efficiency. This underscores the need for precise engine calibration, including adjustments to spark timing and fuel injection, to maintain optimal combustion characteristics. By examining six distinct fuel compositions, this research provides valuable insights into the trade-offs between performance and emissions in alternative fuel applications. The findings support the transition toward biofuels by demonstrating their potential to enhance efficiency and sustainability. As the automotive industry advances in alternative fuel technologies, this study offers guidance for optimizing ethanol-butanol blends to develop cleaner and more efficient combustion systems.

Improved Thermoelectric Properties of SiC Composites with Optimized TiB2 Network Structures

Due to the scarcity of renewable energy sources and the increase in fossil fuel consumption, the development of materials for renewable and sustainable energy production has become an eminent concern, including thermoelectric power generation. Advanced ceramics such as SiC is a desirable alternative material for high-temperature thermoelectric applications. Although SiC has a high Seebeck coefficient, it has relatively low electrical and thermal conductivities, which are undesirable properties for thermoelectric applications. Introducing transitional metal borides as a secondary phase to enhance the electrical conductivity of SiC is a common method. In this study, SiC granules were coated with TiB₂ powders using a simple dry coating method and subsequently subjected to spark plasma sintering to produce composites with conductive network structures. To modify the morphology of the TiB₂ network, SiC granules were classified with particle size ranges of 25-50 µm to 75-100 µm prior to the coating process, Increases of ≈130-500% in electrical conductivity was achieved depending on the matrix granule size distribution, which decreased with increasing SiC granule sizes, showed that the higher concentration of TiB₂ network lowered the percolation threshold causing drastic increases in electrical conductivity. The ZT value increased by ≈50% in the 25-50 µm range.

Controlling of Combustion Process in Energy and Power Systems

As the demand for cleaner energy sources and more efficient power generation technologies continues to grow, the need for advanced combustion control strategies becomes increasingly critical for the zero-carbon emissions target [...]

Prediction of electricity production by small wind power using artificial neural networks

IntroductionWind energy is one of the most significant and rapidly growing renewable energy sources worldwide. It is a clean and environmentally friendly form of energy production, which emits no harmful substances or greenhouse gases during the power generation process. There has been a growing interest in research in the field of wind energy. In this article, an artificial neural network method is used to evaluate the forecasting of wind energy production from a small wind turbine (SWT) installed in central Poland, reflecting inland wind conditions.MethodsA comprehensive set of algorithms and results from simulations are presented. An artificial neural network (ANN) is trained and verified using a large observation dataset. The model includes four input variables: wind speed and direction, rotor speed, air temperature, and one output variable - the power generated by the turbine. Among the available neural networks, Multilayer Perceptron was selected. Genetic algorithms were used to optimize the structure of the model. The Pearson correlation coefficient was used to assess the correspondence between the predicted values and the actual ones. The modeling was carried out in MATLAB, and coefficients such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used to evaluate the prediction error.Results and DiscussionThe learning and testing performance of the neural network model using back propagation with feedback was 96.3% and 97.0%, respectively. Additionally, a sensitivity analysis of the predictive model was performed. The neural network model presented in the article provides accurate predictions of the power generated by a wind turbine. The results obtained confirm the effectiveness of the use of MLP-type neural networks in tasks related to the prediction of energy production in small wind turbines in inland locations.

Cottonseed-Derived Reusable Bio-Carbon Gel Ink for DIW Printing Soft Electronic Textiles.

Soft electronics textiles have garnered global attention for their wearability and promising applications in healthcare, energy devices, and artificial intelligence. Recently, direct-ink-writing (DIW) technology has shown a growing trend because of its controllability, ease of fabrication, and efficiency. However, the design novelty of printable ink for soft electronic textiles is severely hampered by the intrinsic challenges of integrating printability, conductivity, stretchability, biocompatibility, and durability. Herein, a reusable DIW bio-carbon gel ink is proposed for printing soft electronic textiles where cottonseed peptone-functionalized multi-wall carbon nanotubes (CPCNTs) exhibit high dispersibility and reactive surface groups, enabling stable cross-linking with phytic acid (PA) and polyvinyl alcohol (PVA) to form a strong ionic polymer composite. Encouragingly, the gel ink can be directly exploited to design complex circuits and versatile electronics via DIW printing on both polymeric and textile substrates. The viscoelasticity, mechanical recovery, electric properties, robustness, and stretchable architectures enable it to function as flexible circuits, smart sensors, and renewable generators. As demonstrations, multifunctional applications are presented by real-time healthcare monitoring, LED lighting, and power generation. Furthermore, this printable gel ink is effectively assembled into an integrated wearable unit for robot manipulation and real-time gesture recognition, suggesting a significant printing strategy for next-generation wearable electronics.

Rural Renewable Energy Resources Assessment and Electricity Development Scenario Simulation Based on the LEAP Model

This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO2/NOx will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results.

A Review on Operational Development of a Hydroelectric Power Plant

Hydroelectric power plants harness the energy of flowing or falling water to generate electricity, offering a sustainable and renewable energy source. As the water flows through the turbines, it spins them, converting the potential energy of the stored water into mechanical energy. As water flows through the turbines, mechanical energy is converted into electrical energy. Hydroelectric power continues to play a vital role in meeting the world's growing energy demands while supporting climate change mitigation efforts contributing significantly to global electricity production while producing minimal greenhouse gas emissions. This abstract outline the basic operation, advantages, and environmental considerations of hydroelectric power generation, emphasizing its role in achieving cleaner energy goals. Hydroelectric power is one of the oldest and most widely used forms of renewable energy. It utilizes the energy of flowing or falling water to generate electricity. As global energy demands increase and concerns about environmental sustainability grow, hydroelectric power offers a clean, efficient, and reliable source of electricity with minimal greenhouse gas emissions. The generators then transform this mechanical energy into electricity, which is distributed to the power grid for use in homes, businesses, and industries.

Novel Umbrella Optimization Mppt Technique for Solar Photovoltic Systems

Due to the shortcomings of traditional energy sources, there has been a surge in interest worldwide in renewable energy sources like solar, wind, fuel cells, etc., for supplying electricity to isolated locations of the grid in recent decades. Renewable energy sources are environmentally benign, clean, and limitless. These strong energy sources can supply huge grid-isolated demands if they are appropriately harnessed. For this, solar and wind energy are often utilized because of their plentiful supply. These energy sources do have certain drawbacks, though, such as high investment costs and low efficiency. In the current work, isolated DC loads are driven by solar energy. Second, solar PV must always deliver the most power possible to meet the linked load requirement. Several maximum power point tracking (MPPT) controllers with various MPPT strategies are used for this purpose in order to increase PV systems' power generation. All MPPT techniques, however, include a trade-off between precision and stability near the maximum power point, which affects PV system performance.

Dynamic modeling and evaluation of a novel evacuated tube solar collectors-assisted supercritical carbon dioxide Brayton cycle with intercooling and reheating

Abstract The transcritical CO2 Rankine cycle is increasingly recognized as a promising option for power generation in low-grade solar applications. However, there is a liquefaction problem in the condenser due to the CO2’s low condensing temperature. It is very difficult to condense CO2 with the surrounding environment or water. Various approaches, such as implementing a self-condensing transcritical CO2 Rankine cycle, utilizing liquid natural gas, or incorporating CO2 mixtures, have been proposed to address this issue, but these solutions add complexity to the system. A more effective and simpler alternative is to adopt the supercritical CO2 Brayton cycle for low-grade applications. In this study, a novel solar energy-based supercritical carbon dioxide Brayton cycle with reheating and intercooling is proposed. For the heating and reheating process, two collector groups comprised of a total of 20 evacuated solar collectors are utilized. The plant is analyzed in terms of energy and exergy, taking into account Isparta, Turkey’s meteorological data. From the analysis, it is determined that the carbon dioxide temperature could reach a maximum of 265.32 °C. The annual solar energy that falls on the solar collector is determined to be 17,697 kWh, while the annual power generation by the cycle is found to be 547 kWh. The results show that the input heat and the net power are greater than 1.5 kW and 0.3 kW, respectively, for most of the year. Moreover, the solar-based supercritical carbon dioxide Brayton cycle’s energy efficiency is calculated as 3.1%, and only the Brayton cycle’s energy efficiency is found as 11.4%. Graphical abstract

Thermodynamic and economic viability of advanced thermodynamic cycles for power generation in cement plant: case study of Bestway Cement Plant

Thermodynamic and economic viability of advanced thermodynamic cycles for power generation in cement plant: case study of Bestway Cement Plant

Self‐Powered Lower‐Limb Motion Detection with a Piezo‐Electromagnetic Generator

Human limbs generate a lot of energy during movement. To harvest energy during human lower‐limb movement and detect the human body's movement status, a piezo‐electromagnetic collaboration power generator is proposed. It consists of a piezoelectric energy unit and an electromagnetic energy unit. The electromagnetic unit is used for energy harvesting, while the piezoelectric unit is used for self‐powered and self‐sensing detection of lower‐limb movement. The operating principle of the generator is presented, and the theoretical model of the output characteristics is established and simulated. An experimental test platform is built, the detection of self‐powered lower limb movement is accomplished. Results show that the maximum output powers of the electromagnetic and piezoelectric energy units are 14.63 mW and 2.13 mW, respectively. The proposed energy harvester is capable of lighting up 104 Light Emitting Diodes (LEDs) and provides the power supply to a Thermo‐hygrometer. What's more, the self‐powered self‐sensing detection system can realize the detection of the swing frequency of the lower limb and deduce the relationship between the movement speed and the swing frequency, with a detection error of 0.82% for different individuals.

Photocatalytic Aqueous Reforming of Methyl Formate.

Green hydrogen is critical to establish a sustainable energy future as it offers a clean, renewable, and a versatile alternative for decarbonizing industries, transportation, and power generation. However, the limitations of current methods significantly restrict the scope and hinder many of the envisioned applications. This study aims to report on the first example of a 3d-metal-based (Cu) heterogeneous photocatalytic system to produce green hydrogen via dehydrogenation of methyl formate (MF), a reaction previously known to require 4d/5d transition metals. Employing a Cu-based atomically dispersed heterogeneous photocatalyst supported on aryl-amino-substituted graphitic carbon nitride (d-gC3N4), the protocol offers numerous key advantages, including the recyclability of the photocatalyst for >10 cycles without significant activity loss, sustained hydrogen production (>15 days!) with high hydrogen yield (19.8 mmol gcat -1) and negligible CO emission, following an operationally simple, sustainable, and efficient catalytic pathway. Furthermore, the photocatalyst is characterized (using HAADF-STEM, SS-NMR, XAS, EPR, and XPS), all of which clearly demonstrated the presence of single atomic Cu-site. Additionally, comprehensive mechanistic investigations together with DFT calculations allow for a thorough mechanistic rationale for this reaction. It is strongly believed that this atomically dispersed heterogeneous photocatalytic approach will open new avenues for establishing liquid organic hydrogen career (LOHC) technologies.

Fatigue Analysis of the Head Cover of a 100-MW Hydroelectric Power Plant

Abstract The head cover of a 100-MW hydroelectric power machine was broken by fatigue after 35 years of operation. Stress measurements on a unit identical to the failed unit together with the power generation history were used to obtain a stress history and perform a fatigue life prediction. The stress measurement shows that the shaft and the operating ring transfer loads to the head cover, resulting in significant fluctuating stresses, which produce fatigue. The maximum damage and the minimum predicted fatigue life were found at the point close to the operating ring. Additionally, significant corrosion occurred, which reduced the thickness of the plates, increased the roughness, and accelerated fatigue failure. The head covers of the three units must be repaired to improve the contact surfaces with the operating ring, and a periodic program of cleaning, painting, and nondestructive inspection should be implemented.

A data-driven method for IGBT open-circuit fault diagnosis of NPC inverters in three-phase photovoltaic grid-connected systems

Abstract Data-driven methods have exhibited excellent performance and promising prospects in the fault diagnosis of power electronics systems. This article proposed an open-circuit fault diagnosis method based on locally linear embedding (LLE) and regularized extreme learning machine (RELM) for insulated gate bipolar transistor (IGBT) single-tube open-circuit faults of three-level neutral point clamped (NPC) inverters in three-phase photovoltaic grid-connected power generation systems. By building a simulation model of the photovoltaic power generation system, the A-phase output current sample data of IGBT single-tube open-circuit faults under different light intensities are obtained. Subsequently, the sample data is processed by dimensionality reduction and used for the training of the fault diagnosis model. Through optimizing the dimensionality of LLE reduction and the number of hidden layers in the RELM, the model attains optimal diagnostic speed and accuracy. Finally, comparative experiments with other machine learning algorithms demonstrated that the method proposed in this article effectively improved the performance of the IGBT single-tube open-circuit fault diagnosis system for three-level photovoltaic NPC inverters.

Carbon Capture in Coal-Fired Power Plant for Cleaner Energy Management in Indonesia

Indonesia faces a significant challenge in transforming its power sector to meet the national target of achieving Net Zero Emissions (NZE) by 2060. The country’s heavy reliance on coal for base-load electricity generation primarily due to its low cost and domestic availability represents a significant barrier to achieving decarbonization goals. Although global energy trends are shifting toward renewable and low-carbon sources, coal remains a dominant part of Indonesia’s energy mix. In this context, the adoption of transitional energy management such as Carbon Capture and Storage (CCS), particularly Post-Combustion Carbon Capture (PCC), is essential for reducing carbon dioxide (CO₂) emissions while ensuring energy reliability and economic stability throughout the transition period. This study examines the technical and economic feasibility of applying PCC technology at a subcritical coal-fired power plant (CFPP) by integrating the flue gas streams from two 315 MW units in Banten, Indonesia. Each unit emits flue gas containing approximately 14.3% CO₂, with a target capture efficiency of 90%. The research methodology includes literature review, case study analysis, and process simulation using Aspen HYSYS V12. Technical and economic data are drawn from relevant literature and previous PCC implementation cases in CFPPs. The simulation evaluates the integration of a single PCC unit for two combined flue gas sources and calculates both operational and capital costs. The simulation results indicate that the integrated PCC configuration reduces total capital expenditure (CAPEX) from USD 365 million to USD 334 million when compared to the combined cost of individual CCS installations. It also achieves a significantly lower Levelized Cost of Electricity (LCOE), at approximately USD 88.6/MWh, compared to USD 103.6–105.2/MWh in individual unit configurations. In order to attain an IRR target 11%, the integrated system requires a carbon tax of USD 57/tCO₂, which is lower than the USD 73/tCO₂ needed for the single-unit CCS scenario. These outcomes indicate the economic benefits of integrated CCS implementation in advancing Indonesia’s transition toward a low-carbon power generation sector.

Deriving Implicit Optimal Operation Rules for Reservoirs Based on TgLSTM

With the continuous improvement of reservoir projects and the advancement of digital twin basin initiatives in China, rapidly and accurately generating long-term practical reservoir operation schedules has become a key priority for stakeholders. This study proposes a Theory-guided Long Short-Term Memory (TgLSTM) model to extract optimal reservoir operation rules accurately and reliably. Concretely, TgLSTM integrates data-fitting accuracy with the physical constraints of an operation, e.g., water level constraints and minimal discharge constraints, to address the low credibility often observed in conventional LSTM networks. Using the Three Gorges Reservoir during the dry season as a case study, a multi-year hydrological series optimized by particle swarm optimization (PSO) was used to train the TgLSTM network and derive optimized operation rules. Results show that TgLSTM efficiently generates operation schemes close to the theoretical optimum, achieving power generations of 4.27 × 1010 kW·h and 4.19 × 1010 kW·h in two test years, with deviations of only 4.20% and 2.33%, respectively. Compared to traditional LSTM models, TgLSTM is more reliable as it captures key operational characteristics such as terminal water levels and water level fluctuations, maintaining an average ten-day drawdown depth below 1.5 m—significantly lower than the 7 m fluctuations observed with conventional LSTM. Furthermore, comparative analyses against SwR, BP–ANN, and SVM confirm that TgLSTM offers a moderate performance in absolute metrics but is the best to simulate the constrained reservoir operation.

COFLEX: a novel set point optimiser and feedforward–feedback control scheme for large, flexible wind turbines

Abstract. Large-scale wind turbines offer higher power output but present design challenges as increased blade flexibility affects aerodynamic performance and loading under varying conditions. Although flexible structures are considered in terms of (periodic) load control and aerodynamic stability, the impact of flexibility on the aerodynamic response of the blades is currently not fully addressed in conventional control strategies. The current state-of-the-art control strategy is the tip-speed ratio tracking scheme, which aims to maximise power production in the partial-load region by maintaining a constant ratio between blade velocity and wind speed. However, this approach fails under large deformations, where the deflection and structural twist of the blade impact aerodynamic performance. This work aims to redefine the state-of-the-art wind turbine control with the COntrol scheme for FLEXible wind turbines (COFLEX): a novel feedforward–feedback control scheme that leverages optimal operational set points computed by COFLEXOpt, which is a set point optimiser considering the effects of blade deformations on aerodynamic performance and turbine loading. The proposed combined strategy consists of two key modules. The first module, COFLEXOpt, is an optimisation framework that provides controller set points while allowing constraints to be imposed on various operational, structural, and load properties, such as blade deflection and other structural loads. Set points obtained using COFLEXOpt are agnostic to operating regions, meaning that the operating region boundaries are optimised rather than prescribed. The second module is a feedforward–feedback controller and uses the set point mappings generated with COFLEXOpt, scheduled on wind speed estimates, to evaluate feedforward inputs and feedback to correct modelling inaccuracies and ensure closed-loop stability. A set point smoothing technique enables smooth transitions from partial- to full-load operations. The IEA 15 MW turbine is used as an exemplary case to show the effectiveness of COFLEX in maximising rotor aerodynamic efficiency while imposing blade out-of-plane tip displacement constraints. An analysis of the steady-state optimisation results shows that accounting for blade flexibility leads to variable optimal tip-speed ratio operating points in the partial-load region, and the collective pitch angle can be used to counteract blade torsion, maximising power coefficient while complying with imposed constraints. The established controller, tailored to track these optimised set points and operating points, was evaluated through time-marching mid-fidelity HAWC2 simulations across the entire operational range of the IEA 15 MW reference wind turbine (RWT). These simulations, performed under uniform and turbulent wind inflows, demonstrate excellent agreement between optimised steady states and median values obtained from HAWC2 simulations. Furthermore, the generator power shows an increase of up to 5 % in the partial-load region compared to the reference scheme while maintaining blade deflection at a similar level.

Impact of guide vanes on the performance of Savonius wind turbines

The energy efficiency of wind turbines is essential for sustainable energy generation. Therefore, the objective was to evaluate the design and performance of a Savonius turbine, to provide recommendations to optimize its efficiency and power generation capacity in different wind conditions. To address the problem, the energy available to the turbine is analyzed and the concept of the Betz limit is introduced as a reference point for the conversion efficiency. The relationship between wind speed, power coefficient and torque coefficient, fundamental to understanding turbine performance, is examined. Furthermore, the influence of tip speed ratio (TSR) on power extraction is studied, and the torque coefficient is defined as a key performance measure. Experimental tests are carried out in a wind tunnel to evaluate the structural and performance characteristics of the Savonius wind turbine. The results show a correlation between wind speed and turbine performance, with the torque coefficient as the main indicator of efficiency. The presence of guide blades on the Savonius wind turbine can increase the power generated but can decrease the power and torque coefficients. Guide vanes can increase wind speed and electrical energy production, but they can also reduce efficiency by creating a barrier that affects energy capture. Therefore, their use must be carefully evaluated to optimize power production and system efficiency, considering performance objectives and installation site conditions.

Advances in Interfacial Electrostatic Energy Harvesting via Direct Current Triboelectric Nanogenerators

Abstract Interfacial electrostatic energy, widely presents in natural and artificial environments, has long been regarded as a hazardous and uncontrollable energy form due to its erratic discharge behavior. The advent of direct current triboelectric nanogenerators (DC‐TENGs) has opened new approaches for effectively harvesting electrostatic energy during electrostatic discharges and generating unidirectional current outputs without external rectifiers. Compared with conventional alternating current TENGs, DC‐TENGs offer simplified circuit design, and better adaptability for directly driving electronic devices. This review provides a comprehensive summary of the fundamental mechanisms of DC‐TENGs, including air breakdown‐based charge transfer and structural innovations that enable continuous DC output. The key factors affecting performance are systematically analyzed, such as material selection, structural configuration, and environmental conditions, along with recent strategies for performance enhancement. Furthermore, the versatility of DC‐TENGs is highlighted through their wide‐ranging applications in high‐voltage power generation, micro/nano energy sources, wearable electronics, self‐powered sensors, and ocean wave energy harvesting. This review concludes with perspectives on future research directions and practical deployment, emphasizing the potential of DC‐TENGs in enabling sustainable, distributed energy systems for smart environments and next‐generation electronics.

Sub-1000°C Sintering of Protonic Ceramic Electrochemical Cells via Microwave-Driven Vapor Phase Diffusion.

Protonic ceramic electrochemical cells (PCECs) hold significant promise for efficient power generation and sustainable hydrogen production. However, their widespread adoption is hindered by the extreme sintering conditions required for electrolyte densification, often causing performance degradation due to Ba evaporation. Herein, microwave-driven vapor-phase diffusion sintering (MV-sintering) is introduced as an innovative approach for fabricating fully dense, stoichiometric electrolytes at a significantly reduced sintering temperature of 980°C. This method demonstrates broad applicability across proton-conducting oxide electrolytes. The MV-sintered PCEC (MV-PCEC)achieves exceptional power densities of ≈2W cm-2 (600°C) in fuel cell mode, alongside a remarkably high current density of 3.65 A cm-2 at 1.3V (650°C) in electrolysis mode. Digital twin analysis underscores the MV-PCEC's enhanced microstructural features, including finer phase morphology, increased active sites, and improved gas transport. These findings provide critical insights into advancing sintering strategies for high-performance PCECs while mitigating challenges associated with conventional high-temperature processing.