Control of Pollutant Emissions from a Boiler Through the Percentage of Oxygen

The goal of this paper is to obtain an automated system for the control of the quality of the combustion gases of a boiler. The concentration of oxygen present in the combustion gases is detected by means of a lambda sensor, and a proportional PID controller automatically regulates, through an actuator, the air inlet to the boiler, thus ensuring the emission of CO2 and other polluting gases into the atmosphere within the parameters established by the current environmental regulations. The control system has an HMI display and a modular PLC. The results achieved also ensure the reduction of fuel consumption of the boiler.

Test Equipment Adding Electric Supercharger on Injection Machine for Biofuel Optimization

One of the problems that occur in our earth is air pollution and the depletion of fossil fuel stocks. Researchers want to make research on how the fuel burns completely, so that the resulting exhaust gas is environmentally friendly. In addition, this research is expected as an education and comparison before and after the turbofan is given. This study will control the AFR (Air Fuel Ratio) in the intake air to get the right ratio of fuel to air stoichiometry. By controlling the air intake into the engine and adding a turbofan using the PID method. The way the turbofan works is using the lamba sensor as system input, then the value of the lamba sensor is entered into the PID method. From the PID value will determine the speed of the brushless motor which aims to get the best AFR. By looking at the lambda sensor output which is close to the setpoint of 0.5, it is expected to produce a more perfect mixture of air and gasoline.

Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

In four-stroke engine injection system, often called spark ignition (SI) engine, the air-fuel ratio (AFR) is taken from the measurement of lambda sensor in the exhaust. This sensor does not directly describe how much AFR in the combustion chamber due to the large transport delay. Therefore, the lambda sensor is used only as a feedback in AFR control "correction", not as the "main" control. The purpose of this research is to identify the parameters of the non-linear system in SI engines to produce AFR estimator. The AFR estimator is expected to be used as a feedback of the main "AFR" control system. The process of identifying the parameters using the Gauss-Newton method, due to its rapid computation to Achieve convergence, is based on prediction error minimization (PEM). The models of AFR estimator is an open-loop system without a universal exhaust gas oxygen (UEGO) sensors as feedback, called a virtual AFR sensor. The high price of UEGO sensors makes the virtual AFR sensor can be a practical solution to be applied in AFR control. The model in this research is based on the mean value engine models (MVEM) with some modifications. The research dataset was taken from a Hyundai Verna 2002 with the additional UEGO type of lambda sensors. The throttle opening angle (input) is played by stepping on the gas pedal and the signal to the injector (input) is set to a certain quantity to produce the AFR (output) value read by the UEGO sensor. This research produces an open loop estimator model or AFR virtual sensors with normalized root mean square error (NRMSE) = 0.06831 = 6.831%.

Platform to develop exhaust gas sensors manufactured by glass-solder-supported joining of sintered yttria-stabilized zirconia

A manufacturing process for a planar binary lambda sensor is shown. By joining the heating and the sensing components via glass soldering with a joining temperature of 850 °C, a laboratory platform has been established that allows the manufacturing of two independent parts in high-temperature co-fired ceramics technology (HTCC) with electrodes that are post-processed at lower temperatures, as is required for mixed-potential sensors. The final device is compared to a commercial sensor with respect to its sensing performance. Important processes and possible origins of problems as well as their solutions during sensor development are shown, including heater design and joining process.

Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM), to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up.