Exhaust emissions control and engine parameters optimization using artificial neural network virtual sensors for a hydrogen-powered vehicle

Yap, Wai Kean; Ho, Tien; Karri, Vishy

doi:10.1016/j.ijhydene.2012.02.153

Cited by 52 publications

(18 citation statements)

References 41 publications

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“…Among the soft‐computing and data mining tools, artificial neural networks have more application in finding the relationship between inputs and outputs. However, it should be noted that this class of modeling techniques is usually coupled with an iterative optimization algorithm because they propose a black box for prediction Step 3.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Degree of Conformance in Multiresponse–Multistage Systems with a Simulation‐based Metaheuristic

Hejazi

Seyyed-Esfahani

Mahootchi

2014

Quality & Reliability Eng

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In today's manufacturing and service systems, entities are progressed across the several stages of operations wherein one or more quality characteristic may be formed. The quality of final system outputs depends on the quality of intermediate characteristics as well as design parameters in each stage. This paper presents a new mathematical program to simultaneously optimize multiple quality characteristics in multiple stage systems. Multivariate form response surface methodology is applied with iterative seemingly unrelated regression as the estimation method to extract the relationships between the outputs and inputs in each stage. Because the intermediate response variables may act as covariates in the next stages, the probabilistic patterns of the response surfaces are considered by association with the quality of the previous stages. The objective function in the proposed model is the acceptance probability of the outputs based on predefined specification limits. A combination of Monte Carlo simulation and the genetic algorithm is also proposed to solve the final stochastic optimization model. At the end, the applicability of the proposed approach is illustrated by a numerical example. IntroductionR esponse surface methodology (RSM) is a widely used approach in offline quality control and is one of the well-grounded techniques associated with the design of experiments (DOE). It statistically estimates the underlying relationships between quality characteristics, also called response variables, and controllable factors, and then, it mathematically tries to find the optimal setting of the factors that leads to the most desirable quality. Recently, RSM has expanded its capabilities as a data mining tool to approximate the function between inputs and outputs in complex systems.Applications of this method include manufacturing processes such as assembly, machining, and welding as well as service operations such as public transportation, banking systems, and any complex service systems simulated as a black box model.The classic RSM considers only one quality characteristic as a dependent variable. But as systems become more complicated, RSM is also extended in several aspects. In this regard, multiple response variables with different priorities, importance degrees, and scales of measurement 1-3 have been recently focused to achieve compromise solutions. Variance of response surfaces as a criterion for robustness has been studied to reduce total loss of quality. 4 Taking existing correlations among response variables into the account 5 would help to reach more accurate estimation as well as more precise calculations of statistical properties of the responses. There are many systems in which covariates affect the response measurements as commitment variables, so including these noise variables would help to reach more accurate and well-fitted models. 6 Degree of conformance is a measure that helps to judge a system by its rejection rate as an economic index. This measure can be calculated by using stochastic pattern...

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of Degree of Conformance in Multiresponse–Multistage Systems with a Simulation‐based Metaheuristic

Hejazi

Seyyed-Esfahani

Mahootchi

2014

Quality & Reliability Eng

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“…Technologies based on intelligent systems, in particular, artificial neural networks are widespread methods of modeling, optimizing environmental parameters and improving the efficiency of diesel engines [16]. Such technologies are used not only for conventional diesel engines but can also be applied to biodiesels [17] and engines running on liquefied gas [18].…”

Section: Introductionmentioning

confidence: 99%

Method for measuring locomotive diesels characteristics for environmental monitoring tasks

2019

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A method for measuring diesel engines characteristics based on recording the irregularities of time parameters of the crankshaft rotation is proposed. We demonstrate that the change of diesel engines characteristics significantly affects the established norms and limit values of engine emissions of pollutants and harmful substances. The ways of improving the environmental characteristics of diesel engines, including change of fuel composition by adding mixtures are considered. A method of control of the diesel engine parameters consisting in measuring the instantaneous values of the speed of the crankshaft rotation and assessing the deviations of these parameters from normal values has been proposed. The method is implemented in the form of procedures including data collection from sensors installed on the engine, data filtering, and their characteristic analysis. An autonomous measuring device based on the microcontroller has been developed with the help of which data has been collected from the sensors of the 16-cylinder turbocharged engine. We have carried out experiments on process data that determine possibly faulty engine elements that worsen its environmental parameters.

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“…His study showed that as an alternative to classical modeling techniques, the ANN approach can be used to accurately predict the performance, temperature, and other parameters of ICE. Yap et al [6] presented an alternative tool for vehicle tuning applications by incorporating the use of ANN virtual sensors for a hydrogen-powered car. Their objective was to optimize simple engine process parameters to control the exhaust emissions.…”

Section: Introductionmentioning

confidence: 99%

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

et al. 2017

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This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and backpropagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

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Exhaust emissions control and engine parameters optimization using artificial neural network virtual sensors for a hydrogen-powered vehicle

Cited by 52 publications

References 41 publications

Optimization of Degree of Conformance in Multiresponse–Multistage Systems with a Simulation‐based Metaheuristic

Optimization of Degree of Conformance in Multiresponse–Multistage Systems with a Simulation‐based Metaheuristic

Method for measuring locomotive diesels characteristics for environmental monitoring tasks

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

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