Karthik Rajendran

Anaerobic biorefinery: Current status, challenges, and opportunities

Anaerobic digestion (AD) has been in use for many decades. To date, it has been primarily aimed at treating organic wastes, mainly manures and wastewater sludge, and industrial wastewaters. However, with the current advancements, a more open mind is required to look beyond these somewhat restricted original applications of AD. Biorefineries are such concepts, where multiple products including chemicals, fuels, polymers etc. are produced from organic feedstocks. The anaerobic biorefinery concept is now gaining increased attention, utilizing AD as the final disposal step. This review aims at evaluating the potential significance of anaerobic biorefineries, including types of feedstocks, uses for the produced energy, as well as sustainable applications of the generated residual digestate. A comprehensive analysis of various types of anaerobic biorefineries has been developed, including both large-scale and household level applications. Finally, future directives are highlighted showing how anaerobic biorefinery concept could impact the bioeconomy in the near future.

Experimental and economical evaluation of bioconversion of forest residues to biogas using organosolv pretreatment.

The methane potential of forest residues was compared after applying organic solvent, i.e., acetic acid, ethanol, and methanol pretreatments using batch anaerobic digestion (AD). The pretreatments were performed at 190 °C with 50% (V/V) organic solvent for 60 min. The accumulated methane yields after 40 days of AD from pretreated forest residues were between 0.23 and 0.34 m(3) CH4/kg VS, which shows a significant improvement compared to 0.05 m(3) CH4/kg VS, from untreated forest residues. These improvements count up to 50% increase in the methane yields from the pretreated substrates based on expected theoretical yield from carbohydrates. Among the organic solvents, pretreatments with acetic acid and ethanol led to highest methane yields, i.e., over 0.30 m(3) CH4/kg VS. However, techno-economical evaluation showed, pretreatment with methanol was more viable financially. The capital investments of the plant operating 20,000 tons of forest residues varied between 56 and 60 million USD, which could be recovered in less than 8 years of operation.

A Novel Process Simulation Model (PSM) for Anaerobic Digestion Using Aspen Plus

A novel process simulation model (PSM) was developed for biogas production in anaerobic digesters using Aspen Plus®. The PSM is a library model of anaerobic digestion, which predicts the biogas production from any substrate at any given process condition. A total of 46 reactions were used in the model, which include inhibitions, rate-kinetics, pH, ammonia, volume, loading rate, and retention time. The hydrolysis reactions were based on the extent of the reaction, while the acidogenic, acetogenic, and methanogenic reactions were based on the kinetics. The PSM was validated against a variety of lab and industrial data on anaerobic digestion. The P-value after statistical analysis was found to be 0.701, which showed that there was no significant difference between discrete validations and processing conditions. The sensitivity analysis for a ±10% change in composition of substrate and extent of reaction results in 5.285% higher value than the experimental value. The model is available at http://hdl.handle.net/2320/12358 (Rajendran et al., 2013b).

Effect of solids loading on ethanol production: Experimental, economic and environmental analysis

This study explores the effect of high-solids loading for a fed batch enzymatic hydrolysis and fermentation. The solids loading considered was 19%, 30% and 45% using wheat straw and corn stover as a feedstock. Based on the experimental results, techno-economic analysis and life cycle assessments were performed. The experimental results showed that 205±25.8g/L glucose could be obtained from corn stover at 45% solids loading after 96h which when fermented yielded 115.9±6.37g/L ethanol after 60h of fermentation. Techno-economic analysis showed that corn stover at 45% loading yielded the highest ROI at 8% with a payback period less than 12years. Similarly, the global warming potential was lowest for corn stover at 45% loading at -37.8gCO2 eq./MJ ethanol produced.

Phytoremediation of Textile Dye Effluents

Water is the vital source to live and the textile dye effluent is one of the major contaminants present in the wastewater which is highly toxic to all form of lives. Though some effective various methods such as physical, chemical, and biological methods are available to remove the textile dye effluents, phytoremediation is the most economical, eco-friendly, easy to do to degrade the contaminates completely/partially present in effluent. The different plants are found with naturally inhabited metabolic pathways to utilize different dyes and some of the genetically engineered plants are also produced in order to effectively degrade the dyes and to sustain different environmental conditions. Symbiotic relationships between the plant and microbes are also used to help the plants to overcome different kinds of stress. The enzymes like oxidoreductases which are extracted from the plants have shown potent activity against dyes. The significant decrease in color, turbidity, conductivity, total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), and biological oxygen demand (BOD) are taken as indicators of effectiveness of phytoremediation. Several researchers have done extensive studies in phytoremediation area in order to understand the exact mechanism to during treatment of effluents. This chapter mainly focusses on various phytoremediatic methods and its mechanism used in textile effluents treatments.

Biosorptive removal of Zn(II) ions by Pongamia oil cake (Pongamia pinnata) in batch and fixed-bed column studies using response surface methodology and artificial neural network

Design of experiment and artificial neural networks (ANN) have been effectively employed to predict the rate of uptake of Zn(II) ions onto defatted pongamia oil cake. Four independent variables such as, pH (2.0-7.0), initial concentration of Zn(II) ions (50-500 mg/L), temperature (30ºC-50 °C), and dosage of biosorbent (1.0-5.0 g/L) were used for the batch mode while the three independent variables viz. flowrate, initial concentration of Zn(II) ions and bed height were employed for the continuous mode. Second-order polynomial equations were then derived to predict the Zn(II) ion uptake rate. The optimum conditions for batch studies was found to be pH: 4.45, metal ion concentration: 462.48 mg/L, dosage: 2.88 g/L, temperature: 303 K and on the other hand the column studies flow rate: 5.59 mL/min, metal ion concentration: 499.3 mg/L and bed height: 14.82 cm. Under these optimal condition, the adsorption capacity was 80.66 mg/g and 66.29 mg/g for batch and column studies, respectively. The same data was fed to train a feed-forward multilayered perceptron, using MATLAB to develop the ANN based model. The predictive capabilities of the two methodologies were compared, by means of the absolute average deviation (AAD) (4.57%), model predictive error (MPE) (4.15%), root mean square error (RMSE) (3.19), standard error of prediction (SEP) (4.23) and correlation coefficient (R) (0.99) for ANN and for RSM AAD (16.27%), MPE (21,25%), RMSE (13.15%), SEP and R (0.96) by validation data. The findings suggested that compared to the prediction ability of RSM model, the properly trained ANN model has better prediction ability. In batch studies, equilibrium data was used to determine the isotherm constants and first and second order rate constants. In column, bed depth service time (BDST) and Thomas model was used to fit the obtained column data.