Air-fuel Ratio Control Research Articles

Thermal Efficiency of Water Tube Boiler Reviewed from the Effect of Air Fuel Ratio and Water Level Control on Superheated Steam Production

The Cross Section Double Drum Water Tube Boiler is a closed vessel designed to produce steam. This boiler has two main components: the combustion chamber, which serves as the place for fuel combustion, and the steam drum, where the phase change occurs, turning water into steam. The Cross Section Double Drum Water Tube is characterized by tubes arranged transversely to the drum with a 65° incline, aiming to expand the heat transfer area. This study was conducted to determine the effect of the air-to-fuel ratio (17, 19, 21, 23, 25) and water level (20%, 30%, 40%, 50%, 60%) on thermal efficiency. The fuel used in this study is LPG gas. From the results obtained, the optimal air-to-fuel ratio was found to be 23 with a water level of 50%, achieving a thermal efficiency of 72.39% at a temperature of 205°C and a pressure of 9.3 bar. Based on the research conducted, it was concluded that the air-to-fuel ratio and water level significantly affect the boiler's efficiency.

Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm

This study presents a comprehensive investigation into the optimization of PID control parameters for marine dual-fuel engines using an improved particle swarm algorithm. Through the development of a Matlab/Simulink simulation model, the thermodynamic behavior of the engine and the functionality of its control system are analyzed. The PID control parameters for air–fuel ratio control and mode switching control systems are fine-tuned utilizing the improved particle swarm algorithm (PSO). Simulation results demonstrate that the proposed improved PID-PSO approach outperforms traditional PID and traditional PSO-PID control methods in terms of reduced overshoot, minimized steady-state error, faster response times, and improved stability across various operating conditions and response modes. In comparison to traditional PID and PSO-PID controllers, the improved PSO-PID controller reduces the response time by 0.47 s and 0.21 s, the maximum overshoot by 98.43% and 96.05%, and decreases the absolute errors by 87.42% and 90.55%, respectively, in air–fuel ratio control using the step response method. The study's findings offer valuable insights into enhancing the performance and efficiency of marine dual-fuel engines through advanced control strategies.

Research on air-fuel ratio of rotary engines based on variable universe fuzzy control

Abstract A study on the air-fuel ratio control system for the Wankel rotary engine was conducted, where the overall model of the engine was developed in MATLAB/Simulink based on the mean value model. The variable universe fuzzy integral composite control algorithm was applied to feedback control, and the construction and simulation of the control algorithm were completed in MATLAB/Simulink. The results indicate that compared to PID control, variable universe fuzzy control reduced the engine’s fuel injection error by 60.37% and 35.7% under steady-state and transient conditions, respectively. This demonstrates the favorable control effectiveness of variable universe fuzzy control in rotary engines, providing insights and methods for future research on rotary engine control systems.

Transient Performance of Gas-Engine-Based Power System on Ships: An Overview of Modeling, Optimization, and Applications

Liquefied natural gas (LNG) is widely regarded as the midterm solution toward zero-carbon transportation at sea. However, further applications of gas engines are challenging due to their weak dynamic load performance. Therefore, the comprehension of and improvements in the dynamic performance of gas-engine-based power systems are necessary and urgent. A detailed review of research on mechanisms, modeling, and optimization is indispensable to summarize current studies and solutions. Developments in engine air-path systems and power system load control have been summarized and compared. Mechanism studies and modeling methods for engine dynamic performance were investigated and concluded considering the trade-off between precision and simulation cost. Beyond existing studies, this review provides insights into the challenges and potential pathways for future applications in decarbonization and energy diversification. For further utilization of clean fuels, like ammonia and hydrogen, the need for advanced air–fuel ratio control becomes apparent. These measures should be grounded in a deep understanding of current gas engines and the combustion characteristics of new fuels. Additionally, the inherent low inertia feature of electric power systems, and consequently the weak dynamic performance when adopting renewable energies, must be considered and studied to ensure system reliability and safety during transient conditions.

On error-driven nonlinear ESO based control design with application to air–fuel ratio control of engines

In this paper, the tracking control problem of the uncertain control system is studied in the frame of uncertainties estimation and compensation. To eliminate the influence of external disturbances and internal uncertainties on the control system, the error-driven nonlinear extended state observer (EDN-ESO), whose gain is set as an increasing function with respect to the absolute value of estimation error of the system output, is proposed to improve the estimation performance for the “total disturbance”. Next, both the estimation error of proposed ESO and the tracking error of system output are proven to be bounded by selecting the lower bound and variation range of ESO’s bandwidth. Moreover, the convergence of the estimation error as well as the tracking error to zero is proven under certain conditions. Finally, the proposed design method is applied to the air–fuel ratio control system of gasoline engines, and the experimental results show that the proposed EDN-ESO can effectively adjust the observer gain, so as to enhance the tracking performance.

One-Step Ahead Control Using Online Interpolated Transfer Function for Supplementary Control of Air-Fuel Ratio in Thermal Power Plants

Recently, the environmental problem has become a global issue. The air to fuel ratio (AFR) in the combustion of thermal power plants directly influences pollutants and thermal efficiency. A research result was published showing that the AFR control performance of thermal power plants can be improved through supplementary control using dynamic matrix control (DMC). However, online optimization of DMC needs an extra computer server in implementation. This paper proposes a practical AFR control with one-step ahead control which does not use online optimization and can be implemented directly in existing distributed control system (DCS) of thermal power plants. Closed-loop transfer function models at three operating points are independently developed offline. Then, an online transfer function using interpolation of offline models is applied at each sampling step. A simple one-step ahead control with online transfer function is applied as a supplementary control of AFR. Simulations with two different type power plants, a 600 MW oil-fired drum-type power plant and a 1000 MW ultra supercritical (USC) coal-fired once-through type power plant, are performed to show the effectiveness of the proposed control structure. Simulation results show that the proposed supplementary control can effectively improve the conventional AFR control performance of power plants.

Numerical Modeling of Gas-Phase Waste in Incinerator: Focus on Emissions and Energy Recovery under Air-Fuel Ratio and Air Volume Control

Numerical Modeling of Gas-Phase Waste in Incinerator: Focus on Emissions and Energy Recovery under Air-Fuel Ratio and Air Volume Control

In-cylinder air mass flow estimation of gasoline engines based on map self-learning

Accurate in-cylinder air mass flow estimation is important for reducing emissions and improving the fuel efficiency of gasoline engines. Especially, the air-fuel ratio (AFR) control for gasoline engines is directly affected by the air mass flow estimation, which is used in feedforward control. At present, the transient air intake estimation for an engine is much more complex than the steady-state case. In practice, the working conditions of gasoline engines are always changeable to adapt to different traffic conditions, so it is important to improve the air intake estimation under the transient conditions. In order to improve the accuracy of intake air estimation, an appropriate filtering method is designed to eliminate the periodic fluctuations of the signals. And an estimation method based on the map self-learning algorithm is proposed, which considers the full working state of the engine, especially the transient conditions. The convergence of the proposed algorithm is guaranteed theoretically, and the method is designed to realize real-time identification and recursively correct of the volumetric efficiency value. The parameters of the observer are designed and adjusted through experiments to verify the effectiveness of the algorithm. With the continuous recursion of the algorithm, the accuracy of the air intake estimation will be improved and the error will be reduced. At last, the proposed strategy is analyzed and verified by a large number of experiments on the engine test bench to show its promising potentials.

Experimental study on the load control strategy of ammonia-hydrogen dual-fuel internal combustion engine for hybrid power system

The development of hybrid technology and the use of zero-carbon fuels are effective means to improve energy efficiency and reduce carbon emissions. In this study, the Miller cycle spark ignition engine was used, and four load control strategies of throttle, ammonia-hydrogen ratio, air–fuel ratio, and variable valve timing were used to conduct experimental research on the ammonia-hydrogen engine. The experimental results show that the throttle strategy provides the widest output range, and the BMEP is raised from a minimum of 1 bar to 7.4 bar. The ammonia-hydrogen mixing ratio strategy is suitable for cold start conditions, fully utilizing the good combustion characteristics of hydrogen, and at the same time reducing the amount of hydrogen used in the heat engine process as much as possible. The air–fuel ratio strategy provides the highest thermal efficiency with a maximum BTE approaching 40%. It satisfies the energy-saving demand under the single-point working condition of the engine in the series mode. The variable valve timing strategy can realize switching between BMEP between 3.5 bar and 6.5 bar, and keep BTE above 33%. The valve timing strategy can not only provide a wider load adjustment range but also maintain high thermal efficiency. Finally, combining the effects of the four control strategies and the typical operating modes of the hybrid system, this study suggests that the throttle strategy is suitable for the feed mode, the VVT strategy is suitable for the parallel mode, the ammonia-hydrogen mixture ratio strategy is suitable for the cold start condition, and the air–fuel ratio control is suitable for the series mode.

Design of hybrid fault-tolerant control system for air-fuel ratio control of internal combustion engines using artificial neural network and sliding mode control against sensor faults

This paper proposes a novel hybrid fault-tolerant control system (HFTCS) with dedicated non-linear controllers: artificial neural network (ANN) and sliding mode control (SMC) for active and passive parts, respectively. The proposed system can provide both desirable properties of stability to unexpected fast disturbances and post-fault optimal performance. In the active fault tolerant control system (AFTCS) part, the fault detection and isolation (FDI) unit is designed through the use of ANN for the estimation of faulty sensor values in the observer model. In the passive fault-tolerant system (PFTCS) part, the air-fuel ratio (AFR) controller is designed using a robust SMC that allows systems to manage faults in predefined limits without estimation. In the proposed system, SMC will form the passive part to react instantly to faults while ANN will optimize post-fault performance with active compensation. Moreover, Lyapunov stability analysis was also performed to make sure that the system remains stable in both normal and faulty conditions. The simulation results in the Matlab/Simulink environment show that the designed controller is robust to faults in normal and noisy measurements of the sensors. A comparison with the existing works also demonstrates the superior performance of the proposed hybrid algorithm.

Modeling of two-stroke aviation piston engines for control applications

Two-stroke Aviation Piston Engines are multivariable systems with severe non-linear dynamics, making their modeling challenging for control engineers. Although many studies have been conducted on simplified modeling of four-stroke gasoline engines and large-scale two-stroke diesel engines for automobiles and ships, only a few have focused on the general modeling of small two-stroke aviation engines. Thus, extensive research on a general modeling method for two-stroke aviation engines is required. A general non-linear Mean Value Engine Model (MVEM) of two-stroke aviation piston engines is developed. Various parts of the model are represented by appropriate empirical equations that require little engine data and easily fit different engines. The objective is to develop a general and accurate method for rapid engine modeling that captures the main dynamics and can create control systems for two-stroke aviation piston engines. The model is validated using HIRTH-3203 and NU-57 measurement data, and the results show that the issues of fitting simplicity and general applicability are well addressed. Finally, the air-fuel ratio control based on the model predictive control method is carried out on the HIRTH-3203 MVEM, which demonstrates the effectiveness of the proposed MVEM in the controller design and evaluation. The proposed model may support the application of new control technologies, such as adaptive control and intelligent control, in the two-stroke aviation piston engine systems.

Design of Active Fault-Tolerant Control System for Air-Fuel Ratio control of Internal Combustion engine using nonlinear regression-based observer model.

Internal Combustion (IC) engines are prevalent in the process sector, and maintaining sufficient Air-Fuel Ratio (AFR) regulation in their fuel system is crucial for enhanced engine performance, fuel economy, and environmental safety. Faults in the AFR system's sensors cause the engine to shut down, hence, fault tolerance is essential. In order to avoid engine shutdown, this paper offers a novel Active Fault-Tolerant Control System (AFTCS) for air-fuel ratio control of an Internal Combustion (IC) engine in a process plant. In the Fault Detection and Isolation (FDI) unit, the proposed AFTCS uses a nonlinear regression-based observer model for analytical redundancy. The suggested system was simulated in the MATLAB / Simulink environment. The proposed system was tested at two different speeds (300 r/min and 600 r/min) and the results show that the system's response is within the acceptable bound without compromising the stability. The findings also demonstrate the higher fault tolerance capability for sensor defects of the AFR control system, particularly for the MAP sensor (at 300 r/min) in terms of reduced oscillatory response in comparison to the current literature. Compared to the linear regression-based and Genetic Algorithm (GA) based model, the nonlinear regression-based model results in a more accurate estimation of the faulty sensors. The proposed model is also efficient in terms of computation power and response time.

Design of an Adaptive Control to Feed Hydrogen-Enriched Ethanol-Gasoline Blend to an Internal Combustion Engine

In this work, an internal combustion (IC) engine air-fuel ratio (AFR) control system is presented and evaluated by simulation. The control scheme aims to regulate the overall air-fuel ratio (AFRoverall) in an IC engine fueled with a hydrogen-enriched ethanol-gasoline blend (E10) as fast as possible. The control scheme designed and developed in this work considers two control laws, a feedback control law to regulate the hydrogen and adaptive nonlinear control law for controlling the E10 mass flow injection. The main contribution of this work is the reduction of the number of controllers used for controlling the overall air-fuel ratio since other control strategies use two controllers for controlling the E10 mass flow injection. Simulation results have shown the effectiveness of the new control scheme.

Objective Determination of Degradation of Lambda Sensor Using Recursive Least Square Method

<div class="section abstract"><div class="htmlview paragraph">The closed loop air fuel ratio control is one of the essential algorithm to control emissions in internal combustion engine-based vehicle fleets. In order to address this, system and component level diagnostics need to explore extensively. In the presented work, a data-driven method is proposed and implemented to monitor the health of switch type oxygen sensor (lambda sensor). As the response of the lambda sensor present in a vehicle varies over the mile-age which will directly affect the tailpipe emission of the vehicle. Lambda sensor monitoring is the key challenge in OBD-II regulations. A simplified linear model for lambda sensor is derived which includes the lambda sensor gain <i>K<sub>L</sub></i>. The recursive least square estimation algorithm is employed to search the value of the lambda sensor gain <i>K<sub>L</sub></i> for which the model can estimate the lambda sensor output signal. The lambda gain has the correlation with state of health of the lambda sensor. That will be used as an index for age monitoring of the sensor. Proposed algorithm is tested and validated for real city drive condition data set and the results show that the gain <i>K<sub>L</sub></i> converges at different values for different sensor responses. This is an ECU implementable method for real time estimation of sensor gain <i>K<sub>L</sub></i> which lead us to objective determination of degradation in lambda sensor. The estimated value of <i>K<sub>L</sub></i> is validated with three differently aged lambda sensors and mapped to objectively diagnose the health of the sensor.</div></div>

Nonlinear Model Predictive Control for Premixed Turbocharged Natural Gas Engine

In this article, a nonlinear model predictive control (N-MPC) strategy for premixed turbocharged natural gas engine has been proposed. It is applied to genset and the control objective is to track the target speed and air-fuel ratio (AFR), while resisting load disturbance. The main control difficulties are the engine’s strong couplings and variable large AFR delay, and conventional methods (PI) cannot solve these problems well. A premixed fuel model, which can specifically reveal the couplings, has been proposed in this article. N-MPC controller uses this model combined with AFR prediction, coordinating the actuators according to a specified cost function, showing advantages in dealing with this problem over conventional methods. The nonlinear optimization control problem is solved by classic Wilson–Han–Powell sequential quadratic programming method combined with a modified warm start strategy, which uses input compensation to reduce the number of calculation iterations. The simulation results show that the N-MPC has faster response than linear parameter varying model predictive control (LPVMPC), and the warm-start strategy significantly improves computing efficiency. The N-MPC is also verified and compared with conventional methods (PI) and LPVMPC in experiments, which proves that N-MPC is superior to LPVMPC in speed transient performance, and the two MPC are much better than PI in terms of AFR control.

Hybrid Fault-Tolerant Control for Air-Fuel Ratio Control System of Internal Combustion Engine Using Fuzzy Logic and Super-Twisting Sliding Mode Control Techniques

Safety and critical applications employ fault-tolerant control systems (FTCS) to increase reliability and availability in the event of a failure of critical components. Process facilities may employ these technologies to cut down on production losses caused by equipment failures that occur on an irregular or unscheduled basis. Air–fuel ratio (AFR) adjustment in the fuel system of internal combustion engines (ICE) is crucial for enhancing engine efficiency, saving fuel energy, and safeguarding the environment. This paper proposes a novel hybrid fault-tolerant control system (HFTCS) for controlling the AFR in ICEs that combines the features of both an active fault-tolerant control system (AFTCS) and a passive fault-tolerant control system (PFTCS). The fault detection and isolation (FDI) unit is designed using fuzzy logic (FL) as part of an AFTCS to give estimated sensor values to the engine controller when the sensor becomes faulty. Super-twisting sliding mode control (ST-SMC) is implemented as part of a PFTCS to maintain AFR by adjusting the throttle actuator in the fuel supply line under faulty conditions. Lyapunov stability analysis is also performed to make sure that the system remains stable in both normal and faulty conditions. According to the results in the Matlab/Simulink environment, the suggested system stays robust and stable during sensor faults. In faulty situations, it also maintains the AFR at 14.6 without any degradation, and a comparison with previous studies is carried out. The study shows that the suggested approach is an innovative and highly dependable solution for AFR control in ICEs, preventing engine shutdown and output loss for higher profitability.

A Comparative Study of Design of Active Fault-Tolerant Control System for Air–Fuel Ratio Control of Internal Combustion Engine Using Particle Swarm Optimization, Genetic Algorithm, and Nonlinear Regression-Based Observer Model

In this article, three distinct strategies for designing an Active Fault-Tolerant Control System (AFTCS) for Air-Fuel Ratio (AFR) control of an Internal Combustion (IC) engine in a process plant to avoid engine shutdown, are presented. The proposed AFTCS employs a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a Nonlinear Regression (NLR)-based observer model in the Fault Detection and Isolation (FDI) unit for analytical redundancy. A comparison between these three proposed techniques is carried out to determine the least expensive and most accurate approach. The results show that the nonlinear regression produces highly accurate results by consuming very low computational power, and its response time is also very low as compared to GA and PSO. The results obtained show that NLR requires 99.6% and 93.1% less computational time for throttle and MAP estimation, respectively, by reducing the estimation error to as low as 0.01. The simulation of the proposed system is carried out in the MATLAB/Simulink environment. The results prove the superior fault tolerance performance for sensor faults of the AFR control system, especially for the Manifold Absolute Pressure (MAP) sensor in terms of less oscillatory response as compared to that reported in existing literature.

Genetic algorithm based active fault-tolerant control system for air fuel ratio control of internal combustion engines

Internal Combustion (IC) engines are commonly used in the process industry, and proper Air-Fuel Ratio (AFR) regulation in their fuel system is essential for improved engine performance, fuel efficiency, and environmental protection. Since faults in the sensors of the AFR system cause the engine to shut down, fault tolerance is essentially required for them. In this paper, an advanced Active Fault-Tolerant Control System (AFTCS) for AFR control of an IC engine is proposed to prevent the engine shutdown. For analytical redundancy, the proposed AFTCS uses the Genetic Algorithm (GA) based observer model in the Fault Detection and Isolation (FDI) unit to generate estimated value for the faulty sensors using the values of other healthy sensors. The proposed framework was simulated in the MATLAB/Simulink environment to verify its effectiveness. In comparison to previous literature works, the results show that the AFR control system has superior fault tolerance output for sensor faults, especially for the MAP sensor, in terms of less oscillatory response. Finally, a comparison of the proposed GA-based AFTCS was carried out with the existing literature works and its superior performance was elaborated.

Design of active fault-tolerant control system for Air-fuel ratio control of internal combustion engines using fuzzy logic controller.

Fault-Tolerant Control Systems (FTCS) are used in critical and safety applications to improve performance and stability despite failure modes. As a result, costly production losses related to unusual and unplanned shutdowns can be prevented by incorporating these systems in the critical process plant machines. The Internal Combustion (IC) engines are highly used process plant machines and faults in their sensors will cause their shutdown instigating the need to install FTCS in them. In this paper, an Active Fault-Tolerant Control System (AFTCS) based on a Fuzzy Logic Controller (FLC) is suggested to improve the reliability of the Air-Fuel Ratio (AFR) control system of an IC engine. For analytical redundancy, a nonlinear Fuzzy Logic (FL) based observer is implemented in the proposed system for the Fault Detection and Isolation (FDI) unit for nonlinear sensors of the AFR system. Lyapunov stability analysis was used for designing a stable system in both faulty and normal conditions. To evaluate its performance, this system was developed in the MATLAB/Simulink platform. The simulation results show that the developed system is robust under sensor fault conditions, retaining stability with a minimum decrease of AFR. This study's comparison with the existing literature demonstrates that the proposed system is effective for maintaining the AFR in IC engines during sensor faulty conditions thus reducing shutdown of engine and production loss for increased profits.

Laser-induced breakdown spectroscopy for local equivalence ratio measurement in opposed jet methane-air flames

Laser-induced breakdown spectroscopy (LIBS) has been applied to both non-reacting and reacting opposed jet flows to evaluate the measurement accuracy of instantaneous, local air-fuel ratio. However, the LIBS signal is much weaker in flames than in the corresponding non-reacting gases and more work is required to investigate the potential of this technique for instantaneous measurements in reacting flows and quantify measurement uncertainties. The present study reported local equivalence ratio measurements of methane/air flames in an opposed-jet burner, which allowed independent control of flow rate and air-fuel ratio. Images and emission spectra of laser-induced plasma were investigated simultaneously using both an Intensified CCD (ICCD) and a spectrometer with a high degree of spatial and temporal resolution. The influences of the camera delay time, exposure time and laser pulse energy on the LIBS measurements were investigated as well. The dependence of spectral intensity ratios of H/O and C2/CN on air-fuel ratio was quantified over a wide range of conditions extending from pure air to pure fuel. It was found that H/O and C2/CN intensity ratios depend monotonically on the mole fraction of methane in the ranges of 0.0–0.8 and 0.3–1.0 respectively. The presence of a flame within the laser beam led to significant measurement deterioration relative to the corresponding non-reacting flows. This could lead to increased measurement uncertainty, and was therefore corrected by increasing the laser pulse energy and applying a proposed data processing method; the proposed correction method was able to reduce the equivalence ratio measurement uncertainty to be within 10% for mixtures with methane mole fractions lower than 50% and within 15% for mixtures with higher methane mole fraction. The established correlations between the intensity ratios of H/O and C2/CN with local air-fuel ratio allowed measurement of the spatial gradient of air-fuel ratio across opposed jet diffusion flames. The LIBS measurements of air-fuel ratio in non-premixed flames were finally compared successfully with CHEMKIN simulations, which demonstrates the ability of LIBS to accurately measure the spatial gradient of the air-fuel ratio.