Genset Optimization for Biomass Syngas Operation

Although biomass is underrepresented in current methods for power generation, it has great potential to help meet the growing need for clean energy. This chapter details the modification of a gasoline-powered two-stroke genset for operation on syngas from a woodchip-powered gasifier. Generator and engine modifications along with a flexible air/fuel control system are described. Results from genset operation indicate a sustainable power output of 360 W with a biomass consumption rate of approximately 6 kg/hour. Optimum power production was achieved at an air/fuel ratio close to 1. After several hours of operation the engine was disassembled and inspected, revealing significant deposits on the piston and crank case parts, indicating that the engine would require weekly maintenance under such operating conditions

Direct Fuel Injection of LPG in Small Two-Stroke Engines

The commonly used carburetted two-stroke engines in developing countries have high exhaust emission and poor fuel efficiency. To meet more rigid emissions requirements, two-stroke vehicles are typically phase out in favour of four-stroke engines. The problems of ubiquitous legacy two-stroke vehicles remain unsolved by these measures and they are likely to be a major source of transport for many years to come. A number of technologies are available for solving the problems associated with two-stroke engines such as catalytic after-treatment and direct fuel injection (DI). However, these solutions are relatively high cost and have shown only slow market acceptance for applications in developing countries. Research in recent years has demonstrated that direct fuel injection is a well developed and readily deployable solution to existing two-stroke engines. Gaseous fuels such as Liquefied Petroleum Gas (LPG) are considered a promising energy source and in many countries provide fuel cost savings. LPG coupled with DI two-stroke technologies, is expected to be clean and cost effective retrofit solution for two-stroke engines. In this research project, direct injection (DI) of Liquefied Petroleum Gas (LPG) is introduced and tested on a typical two-stroke engine. Results of in cylinder combustion pressure translated to fuel mass fraction burned, engine performance and exhaust emissions are taken and compared for various injection timings from premixed (early injection) to fully direct injection mode (late injection). Results show that DI of LPG effectively reduces exhaust hydrocarbon and can substantially improve the fuel economy of two-stroke engines

Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

Adaptive neuro-fuzzy model with fuzzy clustering for nonlinear prediction and control

Nonlinear systems have more complex manner and profoundness than linear systems.Thus, their analyses are much more difficult.This paper presents the use of neuro-fuzzy networks as means of implementing algorithms suitable for nonlinear black-box prediction and control.In engineering applications, two attractive tools have emerged recently.These two attractive tools are: the artificial neural networks and the fuzzy logic system. One area of particular importance is the design of networks capable of modeling and predicting the behavior of systems that involve complex, multi-variable processes.To illustrate the applicability of the neuro-fuzzy networks, a case study involving air-fuel ratio is presented here.Air- fuel ratio represents complex, nonlinear and stochastic behavior.To monitor the engine conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to capture the nonlinear connections between the air- fuel ratio and control parameters such manifold air pressure, throttle position, manifold air temperature, engine temperature, engine speed, and injection opening time.This paper describes a fuzzy clustering method to initialize the ANFIS

Modern methods in engine knock signal detection

In this paper, a review is given of some of the modern methods in the detection of knock in internal-combustion engines and some comparisons are made between these methods and the effectiveness of each one of them is indicated through a statement of the advantages and disadvantages of each method. In this way it will be possible to clarify how to deal with the original signal and the associated signal noise through some of the modern algorithms in the field of soft computing such as an Artificial Neural Network (ANN), Genetic Algorithms (GA), Wavelet Transform (WT), Fuzzy logic, Supported Vector Machine (SVM) and some statistical methods

Best multiple non-linear model factors for knock engine (SI) by using ANFIS

Knock Prediction in vehicles is an ideal problem for non-linear regression to deal with, which use many of the factors of information to predict another factor. Training data were collected through a test engine for the Malaysian Proton company and in various states of speed.Selected six influential factors on the knocking(Throttle Position Sensor(TPS),Temperature(TEMP),Revolution Per Minute(RPM),(TORQUE),Ignition Timing( IGN),Acceleration Position(AC_POS)), has been taking data for this study and then applied to a single cylinder,output factor (output variable) to be prediction factor is a knock.We compare the performance of resultant ANFIS and Linear regression to obtain results shows effectiveness ANFIS, as well as three factors were selected from six non-linear factors to get the best model by using Adaptive Neuro-Fuzzy Inference System (ANFIS).Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems

New Model for Knock Factors Optimization in Internal Combustion Engine (SI)

The main goal of this paper is to construct a new mathematical model and study the behavior of the factors affecting the problem of knocking in internal combustion engines.Curve fitting technique was used in construction of the model, and also Akaike Information Criterion (AIC) was used as a test in choosing the best model.Factors affecting the problem of knocking have been identified through the use of test engine had promised to do so. The mathematical model was built through real data under certain conditions. Three influential factors (Temp., TPS and RPM) have been taken into consideration. Curve fitting models were used in achieving the goal and then studied the effect of one of the factors in the problem of knocking was investigated.Results obtained through the application of the new model is a low level knocking with increasing temperature (Temp) at the same points in Throttle (TPS), the Revolution Per Minute (RPM), which shows the effectiveness of the new model with non-linear behavior of the factors affecting the knock