Neural network control of air-to-fuel ratio in a bi-fuel engine

Gnanam, G.; Habibi, Saeid; Burton, Richard; Sulatisky, M.T.

doi:10.1109/isic.2003.1253930

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In recent years, the development of expert systems (ES) [2], case-based reasoning (CBR) [3], fuzzy control (FC) [4] and neural networks (NN) [5] has brought new fields to the researchers in engineering applications. Impacting these intelligent control methods on the object of temperature is a focal point in engineering applications at all times.…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligent control of combustion process for ore-roasting furnace

Yan

Chai

et al. 2008

J. Control Theory Appl.

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Because of its synthetic and complex characteristics, the combustion process of the shaft ore-roasting furnace is very difficult to control stably. A hybrid intelligent control approach is developed which consists of two systems: one is a cascade fuzzy control system with a temperature soft-sensor, and the other is a ratio control system for air flow with a compensation model for heating gas flow and air-fuel ratio. This approach combined intelligent control, soft-sensing and fault diagnosis with conventional control. It can adjust both the heating gas flow and the air-fuel ratio in real time. By this way, the difficulty of online measurement of the furnace temperature is solved, the fault ratios during combustion process is decreased, the steady control of the furnace temperature is achieved, and the gas consumption is reduced. The successful application in shaft furnaces of a mineral processing plant in China indicates its effectiveness.

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Section: Introductionmentioning

confidence: 99%

Hybrid intelligent control of combustion process for ore-roasting furnace

Yan

Chai

et al. 2008

J. Control Theory Appl.

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“…For instance, fuzzy logic techniques, dynamic sliding mode control and neural network algorithm have been applied to the airfuel ratio control [8][9][10][11][12][13][14][15][16]. Although these methods contribute to improved ratio control performance, they are based on numerical calculation, and thus, it is time-consuming and troublesome for implementation.…”

Section: Introductionmentioning

confidence: 99%

A ratio control scheme decoupling disturbance response from set-point response

Liu

Zhang

2007

ISA Transactions

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Neural network control of air-to-fuel ratio in a bi-fuel engine

Cited by 2 publications

References 11 publications

Hybrid intelligent control of combustion process for ore-roasting furnace

Hybrid intelligent control of combustion process for ore-roasting furnace

A ratio control scheme decoupling disturbance response from set-point response

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