Energy generation from bioelectrochemical techniques: Concepts, reactor configurations and modeling approaches.

The process industries play a significant role in boosting the economy of any nation. However, poor management in several industries has been posing worrisome threats to an environment that was previously immaculate. As a result, the untreated waste and wastewater discarded by many industries contain abundant organic matter and other toxic chemicals. It is more likely that they disrupt the proper functioning of the water bodies by perturbing the sustenance of many species of flora and fauna occupying the different trophic levels. The simultaneous threats to human health and the environment, as well as the global energy problem, have encouraged a number of nations to work on the development of renewable energy sources. Hence, bioelectrochemical systems (BESs) have attracted the attention of several stakeholders throughout the world on many counts. The bioelectricity generated from BESs has been recognized as a clean fuel. Besides, this technology has advantages such as the direct conversion of substrate to electricity, and efficient operation at ambient and even low temperatures. An overview of the BESs, its important operating parameters, bioremediation of industrial waste and wastewaters, biodegradation kinetics, and artificial neural network (ANN) modeling to describe substrate removal/elimination and energy production of the BESs are discussed. When considering the potential for use in the industrial sector, certain technical issues of BES design and the principal microorganisms/biocatalysts involved in the degradation of waste are also highlighted in this review.

Experiments and adaptive-network-based fuzzy inference system modelling in a hybrid up-flow anaerobic sludge blanket reactor to assess industrial azadirachtin effluent quality.

Experimental investigations were carried out for the treatment of industrial azadirachtin effluent in a hybrid up-flow anaerobic sludge blanket (HUASB) reactor continuously for 115 days in three stages at mesophilic temperature (30 - 35˚C). An adaptive-network-based fuzzy inference system (ANFIS) modelling and statistical regression analysis were applied with the raw data. In the ANFIS modelling as well as in the statistical regression analysis, the operating parameters such as initial pH, influent COD, effluent COD and biogas generation (X1, X2, X3 and X4) were taken as variables and effluent BOD values as a response (Y). The average percentage error (APE) values of ANFIS modelling were 2.18, 12.29, and 0.01%, for stage-I, II and III respectively. These values indicated that ANFIS modelling performed well in all the three stages and provided more accurate results.

Elucidation the influence of design variables on coagulation-flocculation mechanisms in the lab-scale bio-coagulation on toxic industrial effluent treatment.

Bio-coagulants are environmentally friendly substances that have shown potential in removing organic and inorganic contaminants from wastewater from the Imitation Paint Industry. Under the optimized conditions, the use of the three bio-coagulants (of plant origin), Strychnos potatorum, Cactus opuntia and Portunus sanguinolentus (crab) shell (of animal origin) were evaluated, and their removal mechanism was based on kinetic models and adsorption isotherms. The error analysis method was used to find the best isotherm fit. In addition, the kinetic model parameters showed the absence of chemisorption and confirmed the existence of pore diffusion. The interaction between coagulant and pollutant, the type, homogeneity and intensity of the coagulation process, the pollutant absorption capacity of the coagulant were evaluated with the aid of the adsorption isotherm models. From the Pseudo first-order kinetic model an equilibrium pollutant uptake (mg/g) was marked as 598, 554 and 597 for Strychnos potatorum, Cactus opuntia and Portunus sanguinolentus respectively. The better affinity between the pollutants and the bio coagulants were observed through the lower values of Langmuir isotherm constant kL. The adsorption intensity from Freundlich model (nF) were ranged between 1 and 10 for all the listed coagulants, which revealed the physisorption behavior and heterogeneous mechanism of removal. With these results, it would be possible to conduct scale-up studies to adopt the process for practical systems.

Implementation of artificial neural network model for continuous hydrogen production using confectionery wastewater.

In the present study, an artificial neural network (ANN) was implemented to estimate the hydrogen production from confectionery wastewater. From the experimental investigation, it could be concluded that maximum COD removal efficiency of 99% and hydrogen production rate of 6570 mL/d was achieved at 7.00 kg COD/m3d and 24 h HRT. To validate this, a back propagation ANN configuration of 4-12-4-2 was opted. The modelling was performed using the input parameters like time, influent chemical oxygen demand (COD), effluent pH and volatile fatty acids (VFA). The correlation coefficient between the experimental and predicted hydrogen production rate was 0.996. The result of the tested data for hydrogen production rate was successful. The calculated average percentage error (APE) for hydrogen production rate was 0.0004. As the APE values were closer to zero, the trained ANN model fitted well with the experimental data.

Back propagation neural network modelling of biodegradation and fermentative biohydrogen production using distillery wastewater in a hybrid upflow anaerobic sludge blanket reactor.

In a hybrid upflow anaerobic sludge blanket (HUASB) reactor, biodegradation in association with biohydrogen production was studied using distillery wastewater as substrate. The experiments were carried out at ambient temperature (34±1°C) and acidophilic pH of 6.5 with constant hydraulic retention time (HRT) of 24h at various organic loading rates (OLRs) (1-10.2kgCODm(-3)d(-1)) in continuous mode. A maximum hydrogen production rate of 1300mLd(-1) was achieved. A back propagation neural network (BPNN) model with network topology of 4-20-1 using Levenberg-Marquardt (LM) algorithm was developed and validated. A total of 231 data points were studied to examine the performance of the HUASB reactor in acclimatisation and operation phase. The statistical qualities of BPNN models were significant due to the high correlation coefficient, R(2), and lower mean absolute error (MAE) between experimental and simulated data. From the results, it was concluded that BPNN modelling could be applied in HUASB reactor for predicting the biodegradation and biohydrogen production using distillery wastewater.

Biohydrogen production and kinetic modeling using sediment microorganisms of Pichavaram mangroves, India.

Mangrove sediments host rich assemblages of microorganisms, predominantly mixed bacterial cultures, which can be efficiently used for biohydrogen production through anaerobic dark fermentation. The influence of process parameters such as effect of initial glucose concentration, initial medium pH, and trace metal (Fe(2+)) concentration was investigated in this study. A maximum hydrogen yield of 2.34, 2.3, and 2.6 mol H2 mol(-1) glucose, respectively, was obtained under the following set of optimal conditions: initial substrate concentration-10,000 mg L(-1), initial pH-6.0, and ferrous sulphate concentration-100 mg L(-1), respectively. The addition of trace metal to the medium (100 mg L(-1) FeSO4 ·7H2O) enhanced the biohydrogen yield from 2.3 mol H2 mol(-1) glucose to 2.6 mol H2 mol(-1) glucose. Furthermore, the experimental data was subjected to kinetic analysis and the kinetic constants were estimated with the help of well-known kinetic models available in the literature, namely, Monod model, logistic model and Luedeking-Piret model. The model fitting was found to be in good agreement with the experimental observations, for all the models, with regression coefficient values >0.92.

Optimisation and enhancement of biohydrogen production using nickel nanoparticles - a novel approach.

The effect of initial glucose concentration, initial pH and nickel nanoparticles concentration on biohydrogen production was experimented at mesophilic temperature (30-35 °C) using anaerobic microflora in batch tests. It revealed that yield of biohydrogen using nickel nanoparticles with an average size of 13.64 nm was higher than the corresponding control tests. The optimisation of biohydrogen production was carried out by employing response surface methodology (RSM) with a central composite design (CCD). Results showed that the maximum cumulative biohydrogen production of 4400 mL and biohydrogen yield of 2.54 mol of hydrogen/mol of glucose was achieved at optimum conditions, initial glucose concentration of 14.01 g/L at initial pH of 5.61 and nickel nanoparticles concentration of 5.67 mg/L. The results demonstrated that linear and interactive effect of initial substrate concentration and nickel nanoparticles concentration was significant in optimisation of biohydrogen production. Nickel nanoparticles enhanced the biohydrogen production by 22.71%.

Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor.

The performance of an anaerobic hybrid reactor (AHR) for treating penicillin-G wastewater was investigated at the ambient temperatures of 30-35°C for 245 days in three phases. The experimental data were analysed by adopting an adaptive network-based fuzzy inference system (ANFIS) model, which combines the merits of both fuzzy systems and neural network technology. The statistical quality of the ANFIS model was significant due to its high correlation coefficient R(2) between experimental and simulated COD values. The R(2) was found to be 0.9718, 0.9268 and 0.9796 for the I, II and III phases, respectively. Furthermore, one to one correlation among the simulated and observed values was also observed. The results showed the proposed ANFIS model was well performed in predicting the performance of AHR.