Towards improved automation for desalination processes, Part II: Intelligent control

Rao, Ganti Prasada; Al-Gobaisi, Darwish M.K.; Hassan, Ahmed M.; Kurdali, A.; Borsani, R.; Aziz, Mujahid

doi:10.1016/0011-9164(94)00110-3

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…The introduction of non-conventional automation systems has been implemented for MSF to provide the best possible path with optimum energy and chemical consumption for the desired distillate production rate, [1,2]. The critical factor in any decision for the use of new water resources is the water cost.…”

Section: Introductionmentioning

confidence: 99%

PC based SCADA system and additional safety measures for small desalination plants

Avlonitis¹,

Pappas²,

Moutesidis³

et al. 2004

Desalination

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Section: Introductionmentioning

confidence: 99%

PC based SCADA system and additional safety measures for small desalination plants

Avlonitis¹,

Pappas²,

Moutesidis³

et al. 2004

Desalination

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“…This paper describes a methodology and practical guidelines of developing predictive models of large-scale commercial water desalination plants by (1) a databased approach using a neural network based on the backpropagation algorithm and (2) a model-based approach using process simulation with the advanced software tools ASPEN PLUS and SPEEDUP. Specifically, we deal with a multistage flash (MSF) plant [181 760 m 3 per day, or 48 million gallons per day (MGPD)] and a reverse osmosis (RO) plant (56 800 m 3 per day, or 16 MGPD) located in Kuwait and Saudi Arabia, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Desalination plants make good candidates for neural network modeling, because of their computational process complexity, nonlinear behavior, many degrees of freedom, and the presence of uncertainty in the control environment. 1 Quantitative optimization of operating variables could lead to increased production rates, higher product quality, and better plant performance with less energy consumption and lower operating costs. This optimization can also give the operator an early warning of any decline in unit performance.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network and Model-Based Process Simulation

Al-Shayji

Liu

2002

Ind. Eng. Chem. Res.

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This paper presents a methodology and practical guidelines for developing predictive models for large-scale commercial water desalination plants by (1) a data-based approach using neural networks based on the backpropagation algorithm and (2) a model-based approach using process simulation with advanced software tools ASPEN PLUS and SPEEDUP and compares the relative merits of the two approaches. This study utilizes actual operating data from two of the largest multistage flash (MSF) and reverse osmosis (RO) desalination plants in the world. Our resulting neural network and process simulation models are capable of accurately predicting the actual operating data from commercial MSF desalination plants, but the accuracy of a neural network model depends on both the proper selection of input variables and the broad range of data with which the network is trained. A neural network model can handle noisy data more effectively than statistical regression and performs better in predicting the performance variables of both MSF and RO desalination plants. Our neural network model compares favorably with recent neural network models developed by others in accurately predicting actual operating data from commercial MSF desalination plants. When compared to a data-based neural network, a properly validated model-based process simulation (as in the case of MSF desalination plants) can more effectively quantify the effects of varying operating variables on the desalination performance variables. When it is difficult to develop a model-based process simulation (as in the case of RO desalination plants), we can use a data-based neural network to accurately predict the desalination performance variables.

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Computational Intelligence in the Desalination Industry

Cabrera

Carta

2019

Computational Intelligence and Optimization Methods for Control Engineering

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Towards improved automation for desalination processes, Part II: Intelligent control

Cited by 11 publications

References 23 publications

PC based SCADA system and additional safety measures for small desalination plants

PC based SCADA system and additional safety measures for small desalination plants

Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network and Model-Based Process Simulation

Computational Intelligence in the Desalination Industry

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