Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey. Mohd Ali, J.; Hussain, M., a.; Tade, M., O.; and Zhang, J. Expert Systems with Applications, 42(14):5915-5931, Elsevier Ltd, 2015.
Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey [pdf]Paper  Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey [link]Website  abstract   bibtex   
The versatility of Artificial Intelligence (AI) in process systems is not restricted to modelling and control only, but also as estimators to estimate the unmeasured parameters as an alternative to the conventional observers and hardware sensors. These estimators, also known as software sensors have been successfully applied in many chemical process systems such as reactors, distillation columns, and heat exchanger due to their robustness, simple formulation, adaptation capabilities and minimum modelling requirements for the design. However, the various types of AI methods available make it difficult to decide on the most suitable algorithm to be applied for any particular system. Hence, in this paper, we provide a broad literature survey of several AI algorithms implemented as estimators in chemical systems together with their advantages, limitations, practical implications and comparisons between one another to guide researchers in selecting and designing the AI-based estimators. Future research suggestions and directions in improvising and extending the usage of these estimators in various chemical operating units are also presented.
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 title = {Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey},
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 year = {2015},
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 keywords = {Artificial Intelligence,Chemical process systems,Estimator,Soft-sensor},
 pages = {5915-5931},
 volume = {42},
 websites = {http://dx.doi.org/10.1016/j.eswa.2015.03.023},
 publisher = {Elsevier Ltd},
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 abstract = {The versatility of Artificial Intelligence (AI) in process systems is not restricted to modelling and control only, but also as estimators to estimate the unmeasured parameters as an alternative to the conventional observers and hardware sensors. These estimators, also known as software sensors have been successfully applied in many chemical process systems such as reactors, distillation columns, and heat exchanger due to their robustness, simple formulation, adaptation capabilities and minimum modelling requirements for the design. However, the various types of AI methods available make it difficult to decide on the most suitable algorithm to be applied for any particular system. Hence, in this paper, we provide a broad literature survey of several AI algorithms implemented as estimators in chemical systems together with their advantages, limitations, practical implications and comparisons between one another to guide researchers in selecting and designing the AI-based estimators. Future research suggestions and directions in improvising and extending the usage of these estimators in various chemical operating units are also presented.},
 bibtype = {article},
 author = {Mohd Ali, Jarinah and Hussain, M. a. and Tade, Moses O. and Zhang, Jie},
 journal = {Expert Systems with Applications},
 number = {14}
}
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