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

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Development of an open loop fuzzy logic urea dosage controller for use with an SCR equipped HDD engine

Selective Catalytic Reduction (SCR) has been shown to be the most promising exhaust aftertreatment system for reducing oxides of nitrogen in near term in-use applications. SCRs use the ammonia containing compound urea, as a reducing agent. In order to control the urea dosage during transient operation of the engine, sophisticated control strategies are needed. The goal of this study was to design a controller to achieve the maximum NO x emission reduction possible in the transient mode of engine operation, without causing ammonia slip. The development of an open loop, non-sensor based fuzzy logic urea dosage controller is discussed in this thesis. Urea injection values were controlled with \u27maps\u27 based upon the engine speed and engine load, and fuzzy logic was employed as a robust artificial intelligence technique to allow for the development of these maps. Fuzzy logic was utilized to model the complex SCR system and predict the efficiency of NOx conversion. In order to aid in the development of the fuzzy logic SCR model, other methods for generating urea maps were investigated, as well. The first method was an optimization technique, which involved manual testing of the engine to find the optimal urea injection amount. The other method involved injection of urea based upon the average NOx produced. A correction factor was developed and applied to this map to account for losses of ammonia.;The open loop urea map control strategy was implemented without the use of NOx or NH3 sensors. The final fuzzy logic urea map created was able to reduce NOx by 57% over the FTP cycle and 60% over the ETC cycle. This reduction was achieved without causing any significant ammonia slip. The optimized and average NOx urea maps reduced NO x by 67% and 66% over the FTP cycle, but also resulted in large peaks of ammonia slip during the LAFY section. The average NH3 slip seen during the FTP was less than 10 ppm, which was deemed acceptable. The optimized map was also used on the ETC cycle and NOx was reduced by 65% with no significant NH3 slip. The urea maps created for this study appeared to be cycle independent and could be used to control NOx emissions for any transient mode of engine operation

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

A novel fuzzy logic variable geometry turbocharger and exhaust gas recirculation control scheme for optimizing the performance and emissions of a diesel engine

Variable geometry turbocharger and exhaust gas recirculation valves are widely installed on diesel engines to allow optimized control of intake air mass flow and exhaust gas recirculation ratio. The positions of variable geometry turbocharger vanes and exhaust gas recirculation valve are predominantly regulated by dual-loop proportional–integral–derivative controllers to achieve predefined set-points of intake air pressure and exhaust gas recirculation mass flow. The set-points are determined by extensive mapping of the intake air pressure and exhaust gas recirculation mass flow against various engine speeds and loads concerning engine performance and emissions. However, due to the inherent nonlinearities of diesel engines and the strong interferences between variable geometry turbocharger and exhaust gas recirculation, an extensive map of gains for the P, I, and D terms of the proportional–integral–derivative controllers is required to achieve desired control performance. The present simulation study proposes a novel fuzzy logic control scheme to determine appropriate positions of variable geometry turbocharger vanes and exhaust gas recirculation valve in real-time. Once determined, the actual positions of the vanes and valve are regulated by two local proportional–integral–derivative controllers. The fuzzy logic control rules are derived based on an understanding of the interactions among the variable geometry turbocharger, exhaust gas recirculation, and diesel engine. The results obtained from an experimentally validated one-dimensional transient diesel engine model showed that the proposed fuzzy logic control scheme is capable of efficiently optimizing variable geometry turbocharger and exhaust gas recirculation positions under transient engine operating conditions in real-time. Compared to the baseline proportional–integral–derivative controllers approach, both engine’s efficiency and total turbo efficiency have been improved by the proposed fuzzy logic control scheme while NOx and soot emissions have been significantly reduced by 34% and 82%, respectively

Analysis of the Pre-Injection System of a Marine Diesel Engine Through Multiple-Criteria Decision-Making and Artificial Neural Networks

[Abstract] The present work proposes several pre-injection patterns to reduce nitrogen oxides in the Wärtsilä 6L 46 marine engine. A numerical model was carried out to characterise the emissions and consumption of the engine. Several pre-injection quantities, durations, and starting instants were analysed. It was found that oxides of nitrogen can be noticeably reduced but at the expense of increasing consumption as well as other emissions such as carbon monoxide and hydrocarbons. According to this, a multiple-criteria decision-making (MCDM) model was established to select the most appropriate parameters. Besides, an artificial neural network (ANN) was developed to complement the results and analyse a huge quantity of alternatives. This hybrid MCDM-ANN methodology proposed in the present work constitutes a useful tool to design new marine engines

Application of artificial neural network to classify fuel octane number using essential engine operating parameters

Real-time fuel octane number classification is essential to ensure that spark ignition engines operation are free of knock at best combustion efficiency. Combustion with knock is an abnormal phenomenon which constrains the engine performance, thermal efficiency and longevity. The advance timing of the ignition system requires it to be updated with respect to fuel octane number variation. The production series engines are calibrated by the manufacturer to run with a special fuel octane number. Presently, there is no research which takes into account the fuel tendency to knock in real-time engine operation. This research proposed the use of on-board detection of fuel octane number by implementing a simple methodology and use of a non-intrusive sensor. In the experiment, the engine was operated at different speeds, load, spark advance and consumed commercial gasoline with research octane numbers (RON) 95, 97 and 100. The RON classification procedure was investigated using regression analysis as a classic pattern recognition methodology and artificial neural network (ANN) by executing combustion properties derived from in-cylinder pressure signal and engine rotational speed signal. The in-cylinder pressure analysis illustrated the knock-free, light-knock and heavy-knock regions for all engine operating points. The results showed a special pattern for each fuel RON using peak in-cylinder pressure, maximum rate of pressure rise and maximum amplitude of pressure oscillations. Besides, there is a requirement for pre-defined threshold or formula to restrict the implementation of these parameters for on-board fuel identification. The ANN model efficiency with pressure signal as network input had the highest accuracy for all spark advance timing. However, the ANN model with rotational speed signal input only had the ability to identify the fuel octane number after a specific advance timing which was detected at the beginning of noisy combustion due to knock. The confusion matrix for the ANN with speed signal input had increased from 68.1% to 100% by advancing the ignition from -10° to -30° before top dead centre. The results established the ability of rotational speed signal for fuel octane classification using the relation between knock and RON. The implication is that all the production spark ignition engines are equipped with engine speed sensor, thus, this technique can be applied to all engines with any number of cylinders

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time