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Abstract: Mechanical properties of any material are extensively influenced by the parameters such as strain, strain rate, temperature, and its composition. The characteristics of any material such as ductility, strain hardening, strength, dynamic recovery, grain growth, and recrystallization are greatly affected by the influence of various process parameters. So, it is essential to have the knowledge of the constitutive relationships that relate different process variables to flow stress of the deforming material which estimates various parameters such as load, energy, and stress in the metal forming operations. A consistent effort has been gone into developing the constitutive equations for the detailed mathematical description of the flow curves and the aforementioned parameters for years now. Soft computing tools that concern computation of an imprecise environment and model very complex systems those are based on input–output relationship have gained significant attention in recent years. The intricacies of the mathematical modeling of the mechanical properties of the material, enticed the artificial research community to take this as a challenge. One such soft computing tool neural network is applied in this field to predict the behavior accurately. In this paper, a hybrid neural network model better known as RSONFN (Recurrent Self-Organizing Neural Network Model) is applied to predict the flow stress for carbon steels. The RSONFN is having the advantages of the well-established technologies of the artificial intelligence tools such as Fuzzy logic to capture long range data sets and neural networks. The RSONFN structure is a dynamic one as the numbers of its layers as well as the numbers of nodes in each layer of the network are not predetermined. Such an attribute differentiate it from the Multilayer perceptron which is having static structure. The results obtained by this network prove its superiority over other existing tools. [Copyright &y& Elsevier]

The effects of agitation and aeration rates on copolymer poly(3-hydroxybutyrate-co-3-hydroxyvalerate) [P(3HB-co-3HV)] production by Azohydromonas lata MTCC 2311 using cane molasses supplemented with propionic acid in a bioreactor were investigated. The experiments were conducted in a three-level factorial design by varying the impeller (150-500 rev min) and aeration (0.5-1.5 vvm) rates. Further, the data were fitted to mathematical models [quadratic polynomial equation and artificial neural network (ANN)] and process variables were optimized by genetic algorithm-coupled models. ANN and hybrid ANN-GA were found superior for modeling and optimization of process variables, respectively. The maximum copolymer concentration of 7.45 g l with 21.50 mol% of 3HV was predicted at process variables: agitation speed, 287 rev min; and aeration rate, 0.85 vvm, which upon validation gave 7.20 g l of P(3HB-co-3HV) with 21 mol% of 3HV with the prediction error (%) of 3.38 and 2.32, respectively. Agitation speed established a relative high importance of 72.19% than of aeration rate (27.80%) for copolymer accumulation. The volumetric gas-liquid mass transfer coefficient ( k a) was strongly affected by agitation and aeration rates. The highest P(3HB-co-3HV) productivity of 0.163 g l h was achieved at 0.17 s of k a value. During the early phase of copolymer production process, 3HB monomers were accumulated, which were shifted to 3HV units (9-21%) during the cultivation period of 24-42 h. The enhancement of 7.5 and 34% were reported for P(3HB-co-3HV) production and 3HV content, respectively, by hybrid ANN-GA paradigm, which revealed the significant utilization of cane molasses for improved copolymer production. [ABSTRACT FROM AUTHOR]

Abstract: The present work describes the optimization of medium variables for the production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) [P(3HB-co-3HV)] by Azohydromonas lata MTCC 2311 using cane molasses supplemented with propionic acid. Genetic algorithm (GA) has been used for the optimization of P(3HB-co-3HV) production through the simulation of artificial neural network (ANN) and response surface methodology (RSM). The predictions by ANN are better than those of RSM and in good agreement with experimental findings. The highest P(3HB-co-3HV) concentration and 3HV content have been reported as 7.35g/l and 16.84mol%, respectively by hybrid ANN–GA. Upon validation, 7.20g/l and 16.30mol% of P(3HB-co-3HV) concentration and 3HV content have been found in the shake flask, whereas 6.70g/l and 16.35mol%, have been observed in a 3l bioreactor, respectively. The specific growth rate and P(3HB-co-3HV) accumulation rate of 0.29 per h and 0.16g/lh determined with cane molasses are comparable to those observed on pure substrates. [Copyright &y& Elsevier]

Abstract: In the present study, the maximum biomass and polyhydroxybutyrate productions were studied and optimized using suitable carbon and nitrogen sources by bacterial strain Azohydromonas lata MTCC 2311. Among three carbon sources namely, sucrose, fructose, and glucose and four nitrogen sources namely, (NH4)2SO4, NH4Cl, urea, and NH4NO3 studied in shake flask experiments, sucrose and urea were found to be the best carbon and nitrogen sources, respectively. Further, response surface methodology (RSM) and artificial neural network models (ANN) were applied to navigate the experimental data obtained in accordance with the central composite design. The effects of sucrose (3.2–36.82g/L), urea (0.16–1.84g/L), and TE solution (0.32–3.68ml/L) on biomass and PHB concentrations were investigated. The modeling and optimization ability of hybrid ANN-GA had shown higher accuracy in finding optimum concentrations of medium variables than hybrid RSM-GA. Hybrid ANN-GA predicted the maximum biomass concentration (12.25g/L) at the optimum level of medium variables: sucrose, 35.27g/L; urea, 1.55g/L; and TE solution, 0.42ml/L. Whereas, the maximum predicted PHB concentration (5.95g/L) was reported at: sucrose, 35.20g/L; urea, 1.58g/L; and TE solution, 0.36ml/L. The validation with additional set of data shows that the predictive errors (%) in biomass and PHB concentrations were 3.67 and 2.52, respectively for shake flask experiments, whereas, the predictive errors (%) were 13.80 and 14.28, respectively, for bioreactor experiments. [Copyright &y& Elsevier]