Fuzzy identification of reactive distillation for acetic acid recovery from waste water

Araromi, Dauda Olurotimi; Sonibare, Jacob Ademola; Emuoyibofarhe, Justice

doi:10.1016/j.jece.2014.05.008

Cited by 18 publications

(6 citation statements)

References 21 publications

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“…Difficulties can arise from poor knowledge [2]. In some cases, considering some assumptions such as physical properties' constancy, ideality of gas phase and linearization of the nonlinear equations of the model is inevitable, which all impose limitations on the model leading to the reduction of the model's robustness [3].…”

Section: Application Of Ai In Chemical Process Modelingmentioning

confidence: 99%

Application of AI in Chemical Engineering

Hajjar¹,

Tayyebi²,

EghbalAhmadi³

2018

Artificial Intelligence - Emerging Trends and Applications

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A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields.

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Section: Application Of Ai In Chemical Process Modelingmentioning

confidence: 99%

Application of AI in Chemical Engineering

Hajjar¹,

Tayyebi²,

EghbalAhmadi³

2018

Artificial Intelligence - Emerging Trends and Applications

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“…Wastewater treatment processes are highly nonlinear: involving strongly coupled physical, chemical, and biological activities. Hence, predictive models developed from mechanistic approaches are usually dimensionally complex, and the solutions are intractable, computationally intensive, and time-consuming (Mjalli et al 2007;Araromi et al 2014;Nadiri et al 2018). Also, several assumptions often applied to simplify the resulting model introduce some degree of uncertainty which significantly affects the precision (Gontarski et al 2000;Gernaey et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression

Araromi

Majekodunmi

Adeniran

et al. 2018

Environ Monit Assess

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In this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive models of the effluent chemical and 5-day biochemical oxygen demands were developed from measured past inputs and outputs. From a set of candidates, least absolute shrinkage and selection operator (LASSO), and a fuzzy brute-force search were utilized in selecting the best combination of regressors for the GLMs and ANFIS models respectively. Root mean square error (RMSE) and Pearson's correlation coefficient (R-value) served as metrics in assessing the predicting performance of the models. Contrasted with the GLM predictions, the obtained modeling results show that the ANFIS models provide better predictions of the studied effluent variables. The results of the empirical search for the dominant regressors indicate the models have an enormous potential in the estimation of the time lag before a desired effluent quality can be realized, and preempting process disturbances. Hence, the models can be used in developing a software tool that will facilitate the effective management of the treatment operation.

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“…Moreover, greater presentation and potentiality for the nonlinear model was obtained compared to lesser correctness and inadequate fit resulted in linear model modelling for nonlinear system representation [10][11].…”

Section: Introductionmentioning

confidence: 99%