Inverse neural network for optimal performance in polygeneration systems

Hernández, J.A.; Colorado, D.; Cortés-Aburto, Obed; Hamzaoui, Youness El; Hernández-Velázquez, Víctor Manuel; Alonso, Beatriz

doi:10.1016/j.applthermaleng.2011.12.041

Cited by 28 publications

(12 citation statements)

References 19 publications

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“…The inverse ANN was proven recently to be efficient tool to optimize several processes (Hernández, 2009;Cortés et al, 2009;;El hamzaoui et al, 2011;Labus et al, 2012;Hernández et al, 2013;Laidi and Hanini, 2013). Nevertheless, applications of such ANN based optimization procedure are still lacking in the field of soil/plant problems.…”

Section: Discussionmentioning

confidence: 99%

“…amendments) that have resulted in this given output (metal concentrations in the plant leaves). Recently, several applications based on inverse neural network models referred as (ANNi) were developed by several authors to optimize the performance of polygeneration systems parameters (Hernández et al, 2013), to control the strategy for absorption chillers (Labus et al, 2012), to optimize the operating conditions for compressor performance (Cortés et al, 2009), to optimize the operating conditions for heat and mass transfer in foodstuffs drying (Hernández, 2009), to predict the chemical oxygen demand removal during the degradation of alazine and gesaprim commercial herbicides (El hamzaoui et al, 2011) and to optimize solarassisted adsorption refrigeration system (Laidi and Hanini, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…In the studies of (Hernández, 2009;Cortés et al, 2009;;El hamzaoui et al, 2011;Labus et al, 2012;Hernández et al, 2013;Laidi and Hanini, 2013), the authors generated the resulting corresponding mathematical equations obtained from the trained direct ANNs representing the investigated processes behaviors and used optimization algorithms based on these equations to assess the optimal input parameters. In general, optimization involves finding the minimum or/and maximum of these n objective functions subjected to some constraints.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

Hattab

Motelica-Heino

2014

Journal of Geochemical Exploration

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To cite this version:Nour Hattab, Mikael Motelica-Heino. Application of an inverse neural network model for the identification of optimal amendment to reduce Copper toxicity in phytoremediated contaminated soils. Journal of Geochemical Exploration, Elsevier, 2014, 136, pp.14-23 AbstractArtificial neural network ANN prediction approaches applied to the modeling of soil behavior are often solved in the forward direction, by measuring the response of the soil (outputs) to a given set of soil inputs. Conversely, one may be interested in the assessment of a given set of soil inputs that leads to given (target) soil outputs. This is the inverse of the former problem. In this study, we develop and test an inverse artificial neural network model for the prediction of the optimal soil treatment to reduce copper ( Cu) toxicity assessed by a given target concentration of Cu in dwarf bean leaves (BL) from selected soil inputs. In this study the inputs are the soil pH, electrical conductivity (EC), dissolved organic carbon (DOC) and a given target toxicity value of Cu, whereas the output is the best treatment to reduce the given toxicity level. It is shown that the proposed method can successfully identify the best soil treatment from the soil properties (inputs).Two important challenges for optimal treatment prediction using neural networks are the nonuniqueness of the solution of the inverse problem and the inaccuracies in the measurement of the soil properties (inputs). It is shown that the neural network prediction model proposed can overcome both these challenges. It is also shown that the proposed inverse neural network method 2 can potentially be applied with a high level of success to the phytoremediation of contaminated soils. Before large-scale application, further validation is needed by performing several experiments and investigations including additional factors and their combinations to capture the complex soil behavior.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

Hattab

Motelica-Heino

2014

Journal of Geochemical Exploration

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“…Recently, several applications based on inverse neural network models referred as (ANNi) were developed by several authors to optimize the performance of polygeneration systems parameters [49], to control the strategy for absorption chillers [50], to optimize the operating conditions for compressor performance [51], to optimize the operating conditions for heat and mass transfer in foodstuffs drying [52], to predict the chemical oxygen demand removal during the degradation of alazine and gesaprim commercial herbicides [53] and to optimize solarassisted adsorption refrigeration system [54].…”

Section: Predict Structural Parameters (Anni)mentioning

confidence: 99%

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

et al. 2015

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Today numerous consumers consider thermal comfort to be one of the most significant attributes when purchasing textile and apparel products, so there is a need to develop a model able to simulate objectively the consumers' perception. The global thermal comfort of stretch knitted fabrics is a multi-criteria phenomenon that requires the satisfaction of several properties at the same time. In this paper, we used the desirability functions to evaluate the satisfaction degree of global thermal comfort. Statistical method was used to investigate the interrelationship among knit thermo-physical properties, and group them into factors. Two models of artificial neural network (general and special) have been set up to predict the global thermal comfort from structural parameters (inputs) of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. By combining the strengths of statistics and fuzzy logic (data reduction and information summation) also a neural network (self-learning ability), hybrid model was developed to simulate the consumer thermal comfort perception. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown optimum input parameter. Finally, this forecasting can help industrials to anticipate the consumer's taste. Thus, they can adjust the knitting production parameter to reach the desired global thermal comfort to satisfy this consumer.

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“…The inverse neural network is applied to optimization of operating conditions or parameters in energy processes in Ref. [8].…”

Section: Introductionmentioning

confidence: 99%

Control of a heat exchanger using neural network predictive controller combined with auxiliary fuzzy controller

Vasičkaninová,

Bakošová

2015

Applied Thermal Engineering

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Inverse neural network for optimal performance in polygeneration systems

Cited by 28 publications

References 19 publications

Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

Control of a heat exchanger using neural network predictive controller combined with auxiliary fuzzy controller

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