Journal Article10.1016/S0360-1285(03)00058-3
Artificial intelligence for the modeling and control of combustion processes: a review
632
TL;DR: How AI techniques might play an important role in modeling and prediction of the performance and control of combustion process is illustrated to testify to the potential of AI as a design tool in many areas of combustion engineering.
read more
About: This article is published in Progress in Energy and Combustion Science. The article was published on 01 Jan 2003. The article focuses on the topics: Expert system & Systems modeling.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Artificial intelligence techniques for photovoltaic applications: A review
Adel Mellit,Soteris A. Kalogirou +1 more
TL;DR: The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application, mainly because of their symbolic reasoning, flexibility and explanation capabilities.
909
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
TL;DR: Developing a convolutional neural network to learn features directly from frequency data of vibration signals and testing the different performance of feature learning from raw data, frequency spectrum and combined time-frequency data demonstrate that the proposed method is able to learning features adaptively from frequencyData and achieve higher diagnosis accuracy than other comparative methods.
690
Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source
TL;DR: In this paper, an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it's needed.
582
Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network
TL;DR: In this paper, an artificial neural network (ANN) model was used to predict the brake power, torque, specific fuel consumption and exhaust emissions of a two-cylinder four-stroke diesel engine.
550
Artificial intelligence techniques for sizing photovoltaic systems: A review
TL;DR: An overview of the AI-techniques for sizing photovoltaic (PV) systems: stand-alone PVs, grid-connected PV systems, PV-wind hybrid systems, etc.
454
References
Active control of combustion instabilities on a rijke tube using neural networks
R. Blonbou,A. Laverdant,Stéphane Zaleski,Stéphane Zaleski,P. Kuentzmann +4 more
- 01 Jan 2000
TL;DR: In this paper, an internal model control scheme for nonlinear systems that uses two artificial neural networks was developed to predict the response of the burner to the control action and used this prediction to update the controller's parameters.
An investigation of optimum control of ignition timing and injection system in an in-cylinder injection type hydrogen fueled engine
TL;DR: In this article, a fuzzy-neural network controller combining with an ignition adaptive controller is applied to the engine in order to optimally control ignition timing, injection timing and cycle amount of hydrogen injection.
Thick film pellistor array with a neural network post-treatment
TL;DR: In this article, the authors exploited the differential activity of palladium and platinum by using two screen-printed pellistors, one based on Pd and the other on Pt, to achieve selectivity to methane.
Reconstructing cylinder pressure from vibration signals based on radial basis function networks
TL;DR: An approach to reconstruct internal combustion engine cylinder pressure from the engine cylinder head vibration signals, using radial basis function (RBF) networks, is presented and the prediction capabilities of the trained RBF network model are validated against measured data.