Artificial neural‐network‐assisted stochastic process optimization strategies

Nandi, Somnath; Ghosh, Soumitra; Tambe, Sanjeev S.; Kulkarni, Bhaskar D.

doi:10.1002/aic.690470113

Cited by 42 publications

(15 citation statements)

References 32 publications

(23 reference statements)

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“…Thus, for an objective function maximization problem, a thorough exploration of the solution space is necessary to secure a solution that corresponds to the tallest local or the global maximum [6]. Accordingly, in the present study the GA-based optimization simulations were repeated by using each time a different randomly initialized population of the candidate solutions.…”

Section: Ga-based Optimization Of the Mlp Modelmentioning

confidence: 99%

“…The most widely utilized ANN paradigm is the multi-layered perceptron (MLP) that approximates non-linear relationships existing between multiple causal (input) process variables and the corresponding dependent (output) variables [6]. Once an ANN-based process model with fairly good generalization capability is constructed, its input space can be optimized appropriately to secure the optimal values of process variables.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of fermentation media for exopolysaccharide production from Lactobacillus plantarum using artificial intelligence-based techniques

Desai¹,

Akolkar²,

Badhe³

et al. 2006

Process Biochemistry

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Section: Ga-based Optimization Of the Mlp Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of fermentation media for exopolysaccharide production from Lactobacillus plantarum using artificial intelligence-based techniques

Desai¹,

Akolkar²,

Badhe³

et al. 2006

Process Biochemistry

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“…The training and test set RMSEs corresponding to the optimal model were 0.0377 and 0.0489, respectively. Once an optimal MLP-based model was obtained, in the next phase its input space comprising the three operating variables was optimized using the GA and SPSA methods (Nandi et al, 2001(Nandi et al, , 2002. The GA/SPSA procedure for this optimization remained same as used in optimizing the input space of the GP-based model.…”

Section: 2 Gp-based Fermenter Modelingmentioning

confidence: 99%

Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation

Cheema

Sankpal

Tambe

et al. 2002

Biotechnology Progress

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This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.

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“…On the other hand, ANN is suitable for developing bioprocess models and the ANN models are exclusively data-based. The most widely utilized ANN architecture is multi-layered that approximates non-linear relationships existing between multiple input process variables and the corresponding dependent (output) variables (Nandi et al, 2001). ANNs were successfully used to model the results of biogas production and chemical oxygen demand (COD) removal with an upflow anaerobic sludge blanket reactor (UASB) (Mu & Yu, 2007) and an expanded granular sludge bed (EGSB) reactor (Guo et al, 2008).With RSM and ANN, the interactions of influencing parameters on methane gas can be evaluated.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Methane Gas Production From Co-digestion of Food waste and Poultry Manure Using Artificial Neural Network and Response Surface Methodology

Yusof¹,

Man²,

Rahman³

et al. 2014

JAS

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Food waste with high carbohydrate content is considered as a suitable substrate for fermentation of methane gas. In this study, co-digestion of poultry manure (PM) and food waste (FW) was used. Response surface methodology (RSM) and artificial neural network (ANN) were applied to optimize parameters of co-digestion of PM and FW at different ratios, initial pH values and temperatures. A comparative analysis was done using RSM and ANN in a predictive model of the experimental data obtained in accordance with the central composite design. The combined effects of the independent variables (ratio, pH and temperature) as the most significant parameters of methane fermentation of PM and FW were investigated. Optimization using RSM and ANN showed a good fit between the experimental and the predicted data as elucidated by the coefficient of determination with R 2 values of 0.991 and 0.998, respectively. Quadratic RSM predicted the maximum methane yield to be 537 mL CH 4 /g VS at the optimal conditions; ratio 80:20 (PM : FW); temperature 35 °C; and initial pH 7.11. The maximum predicted methane yield by the ANN model was 535.82 mL CH 4 /g VS at the following conditions; ratio of poultry manure to food waste 80:20; temperature 35 °C; and pH 7.00. The verification experiments successfully produced 538 mL CH 4 /g VS within 14 days of incubation. These experiments indicated that the developed model was successfully used to predict the fermentable methane production.

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Artificial neural‐network‐assisted stochastic process optimization strategies

Abstract: This article presents two hybrid robust process optimization approaches integrating artificial neural networks (ANN) and stochastic optimization formalisms -genetic algorithms (GAS) and simultaneous perturbation stochastic approximation (SPSA

Cited by 42 publications

References 32 publications

Optimization of fermentation media for exopolysaccharide production from Lactobacillus plantarum using artificial intelligence-based techniques

Optimization of fermentation media for exopolysaccharide production from Lactobacillus plantarum using artificial intelligence-based techniques

Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation

Optimization of Methane Gas Production From Co-digestion of Food waste and Poultry Manure Using Artificial Neural Network and Response Surface Methodology

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