Agriculture biomass or waste are alternative feedstock to energy and chemical production. Leftover straw, stalk and husk of a crop plant are the crop residues after its harvesting (Devi et al., 2017). Agricultural wastes are agriculture crop residues i.e. residues from group of cereals, oilseeds, pulses, horticulture, and other crops. Most of the agricultural residues used for animal feeding, fuel for domestic application or gasification (Hiloidhari et al., 2014; Report RRECL, 2017).

Around 25% of biomass from crop residues is burnt on a global basis (Devi et al., 2017). Its on-farm burning, categorized as underutilization of resources and leading to environmental impacts (Hiloidhari et al., 2014; Kumar et al., 2018). Agricultural crop residues can be utilized into biogas production, textile making process, fuel and manure, etc. to mitigate the problem of on farm in-situ burning of surplus crop residues which is a source of GHG, smoke, aerosols, and particulate matters etc. (Nguyen et al., 2016; Devi et al., 2017). Animal dung from cattle, goats, ships, and poultry are highly loaded from organic matter content and having huge pollution potential of methane gas. It is utilized as potential substrate or inoculum in co-digestion in the biogas plants (Bundhoo et al., 2016). Also, Shah et al. (2015) has reported the anaerobic co-digestion of blends of maize residues, water hyacinth, giant reed, and poultry litter for biogas production at community scale.

India generates crop residues 686 MT annually, out of which 34% of crop residue is estimated as surplus (Hiloidhari et al., 2014). For Rajasthan state, agricultural activity generates 52.6 MT/year residues out of which 9.25% remain surplus (Report RRECL, 2017). The surplus fraction of residue availability from cereals is 29%, from oilseeds is 18%, and 23% from pulses crop in Rajasthan (Hiloidhari et al., 2014). Wheat (Triticum) crops cultivation is 2nd highest among cereals in the country, and 5th highest in the Rajasthan state. Wheat straw [Crop Residue Ratio (CRR) = 1.5] production is 15.8 MT/year having 30% share to total biomass generation in the state (Annual Report MA FW, 2017; Report RRECL, 2017). Wheat straw has composition of 30.1–39.2% cellulose, 22.2–34.0% hemicellulose, and 6.5–22.1% lignin (Chandra et al., 2012a,b; Bolado-Rodríguez et al., 2016; Yadav et al., 2018).

MSW generation is increasing in almost all cities with urbanization and population growth, accompanied by its economy. MSW handling is one of the chief issue facing Indian cities. Its poor and incautious management entails enormous impacts on public health, environment, and climate change (Shekdar, 2009; Yadav and Samadder, 2017; Gollapalli and Kota, 2018). Currently, India generates 62 MT MSW per year where collection and treatment efficiency is around 90 and 27%, respectively, which means 79% MSW directly goes to unsanitary landfilling (Planning Commission Report, 2014; MSW Report, 2017; Yadav and Samadder, 2017). Jaipur, one of the metro cities of India, generates 1,000 tons per day (TPD) MSW, 31% share alone in total state MSW generation with ~3.04 million population (Census, 2011; Municipal solid waste, 2017). Waste to energy potential in India with organic and inorganic portions are open for conversion to biofuel and for power generation. Presently, India has 2,554 MW potential as renewable energy potential from waste generated i.e., MSW and waste water (Kalyani and Pandey, 2014; MNRE Annual Report, 2017).

Improved income and population in a city led to a changed lifestyle of urban dwellers, and also causing an escalation in waste quantity and MSW composition altered. Knowledge of the composition of MSW is important for selecting the suitable waste processing and disposal practices since MSW volume and its composition differs considerably with the places having changes in food habits, cultural traditions, lifestyles, socio-economic conditions, and climate (Yay, 2015; Mboowa et al., 2017). OFMSW has composition around 17.5% cellulose, 10.7% hemicellulose, 9.6% lignin, 37.8% raw fiber, 7.7% protein, and 9.6% fat or oil (Rao and Singh, 2004). However, its composition varies with their nutrient content due to its heterogeneous nature with respect to place to place even in the same city. Through AD process, OFMSW is capable of generating 300–400 m³ biogas/ ton of VS of OFMSW (Dhar et al., 2017).

The agro-industrial sector represents one of the important sectors of the economy, responsible for processing or production of different foodstuffs and meanwhile also generating large quantity of waste. Major agro-industrial waste comes from food processing, juice and wine industry, edible oil refineries, slaughterhouse, and dairy processing. Many food processing wastes are sent to animal feeding, and chemical production and rest goes to landfilling (Bundhoo et al., 2016; Pellera and Gidarakos, 2017). Although glycerine as ideal co-substrate being pure and 100% degradability, other products like cheese whey, olive mill waste, sugar by-products, have been used for co-digestion with positive results (Mata-Alvarez et al., 2014). Here, Mung bean husk, groundnut shell, guar husk and rice bran have been studied as agro-industrial waste for biogas potential.

Mung bean (Vigna radiata) production is 4,77,245 tons sharing 20% of pulse production as 2nd highest production after gram pulse which has 44% share in pulses production in the state (Rajasthan Agri-Statistics Report, 2017). Groundnut (Arachis hypogea) crop production is 8,08,143 tons production in Rajasthan with 15.56% share to country (Rajasthan Agri-Statistics Report, 2017). Its shells (CRR = 0.3) are discarded waste, which is a carbohydrate rich biomass having composition 25.57% carbohydrate, crude protein 4.43% and lipid 0.50%, crude fiber 59%, ash content 2.5%, and moisture 8% (Abdulrazak et al., 2014; Hiloidhari et al., 2014). Guar or cluster bean, with the botanical name Cyamopsis tetragonoloba drought tolerant legume grown in the Rajasthan state of India. Guar meal is a protein and fiber rich nutritional food for livestock containing germ and hull part of it (Janampet et al., 2016). Rajasthan produces Guar crop 2,204,931 tons annually in 2014–15. Its residues are stalks and husk. Rice (Oryza sativa) crop is most widely consumed food grain in the world. This cereal has 103.73 MT cultivation in India being 2nd highest in India after sugarcane crop. Its production is 2,84,131 tons annually in Rajasthan state. Residues from rice milling industry are rice husk and bran. (Rajasthan Agri-Statistics Report, 2017). Rice bran has composition around 4.6% cellulose, 8.4% hemicellulose, 2.8% lignin, 28.5% starch, and 13.9% protein (Favaro et al., 2017).

The AD is biological process categorized in the effective waste management as well as waste to energy technologies. As it avoids the natural emission of greenhouse gases like CH₄ and CO₂ from self-degradation of organic waste. Main products of AD process are CH₄ for renewable energy production and a digestate as bio-fertilizer for soil amendment (Vivekanand et al., 2013; Bundhoo et al., 2016). As AD owing several benefits concerning environment, economy and maintenance, also have been used for processing agricultural waste, MSW, sewage sludge, livestock effluents, agro-industrial waste, therefore AD process being dealt in this study.

The AD consists of four consecutive processes i.e., Hydrolysis, acidogenesis, acetogenesis, and methanation as shown in Figure 1. In first phase, large polymers breakdown into monomers, carbohydrates into simple sugar, fat into fatty acid, and glycerol, protein into amino acid and peptide. In second phase, these monomers break down into carbonic acid, alcohols, H₂, CO₂, NH₃. In this phase, O₂ and CO₂ consumed by facultative bacteria to create the anaerobic condition. In third phase i.e., acetogenesis, previous phase products are converted into acetic acid, H₂ and CO₂. This acetic acid and hydrogen are converted into methane by different methanogens i.e. archea in the last phase of AD i.e., methanogenesis (Kumar et al., 2018; Negi et al., 2018).

Nutritional imbalance and operating factors like biodegradability and chemical composition of single substrate make its direct utilization tough. Therefore, different nutrition composition substrates are digested together, also biogas yield gets improved. Anaerobic co-digestion provides benefits like process stabilization, pH and buffer capacity, avoid inhibitory or toxic compounds (formation of Volatile fatty acids (VFA)/ammonia), microorganism's good synergistic effect (Mata-Alvarez et al., 2014; Hagos et al., 2017; Pellera and Gidarakos, 2017). AcoD is a technique for simultaneous treatment of different organic residues proving better synergism through diverse co-substrates and microbial activity by improving methane yield in process. Suitable co-substrate is that which compensate with excess metabolites produced by another substrate which inhibit the methanogens in AD (Zhang et al., 2016). In simple words, AcoD is performed to balance the C/N ratio of the feedstock.

BMP test is a batch experiment of AD determining the biomethane potential of a substrate. This test is performed in laboratory for examining the biodegradability and methane conversion efficiency of organic substrates, assessing different combination of co-substrates, feasibility of different biomasses for AD, also helps in achieving optimal condition for AD process (Cho et al., 2013; Vivekanand et al., 2013; Hagos et al., 2017). A feasibility study of organic waste energy potential in college campus shows mixed food and green waste has biogas potential of 400–660 ml/g VS of the mixed substrate (Paritosh et al., 2018).

Kacprzak et al. (2010), experimented combination of agriculture waste (corn silage, carrot residues), agro-industrial waste (beet pulp silage and cheese whey) with industrial waste: glycerine (waste from biodiesel production), resulting highest methane yield with mixture of corn silage, cheese whey and glycerine (Kacprzak et al., 2010; Mata-Alvarez et al., 2014). Blending of few agro-industrial waste i.e. brewery spent grain, carbonated soft drink sludge, with either powdered rice husk or soya bean cake resulted into enhanced biogas production than individuals (Uzodinma et al., 2007). Vivekanand et al. (2018) co-digested manure, fish ensilage and whey as multi feed-stocks and produced 84% higher methane production than individual substrate digestion. Food waste co-digestion with straw (containing straw of wheat, maize, and sorgos) showed 39.5 and 149.7% augmented methane yield at 5:1 mixing ratio as compared to mono-digestion of food waste and straw, respectively (Yong et al., 2015). Co-digestion of OFMSW and rice straw in the ratio of 2:1 yielded 57% higher methane (403 ml/g VS) than the other combinations (Negi et al., 2018). Batch test of co-digestion of OFMSW with pretreated sludge and rice straw experimented at different blending ratio, and ratio 3:0.5:0.5 yields maximum biogas 558 ml/g VS (Abudi et al., 2016). Shekdar (2009) has reported the different treatment methods for improving the biogas production from OFMSW. MSW, bovine slaughterhouse, manure and crop residues were mixed and evaluated for methane production in mesophilic and thermophilic condition and mesophilic condition results showed 57% lower methane in comparison with the thermophilic condition (Pagés-Díaz et al., 2013). Methane yield from corn straw and manure get increased up to 22.4% by means of mixing another substrate i.e., fruit and vegetable waste (Wang et al., 2018).

The organic fraction of municipal waste and agro-industrial waste are mostly investigated and used for co-substrates with manure (Mata-Alvarez et al., 2014; Abudi et al., 2016). Selection of suitable co-substrates and the ratio of mixing are key factors aiming synergism to enhanced methane production. Municipal, agro-industrial and agricultural waste have been individually utilized as a source of energy through this technique. So far little work performed regarding synergistic effect from these three different sector waste or biomass (Sharholy et al., 2008; Vivekanand et al., 2018).

Utilizing bioenergy from biomass with higher efficiency is also a major concern, for this different pretreatment techniques are applied. Lignocellulosic biomass composed of cellulose, hemicellulose, and lignin, where lignin has the amorphous 3D structure of phenylpropanoid units covering cellulose fibers hindering access from enzymes. For deconstructing lignin structure, biological, physical or chemical, or physiochemical pretreatment techniques are available (Bussemaker and Zhang, 2013; Kumar et al., 2018; Yadav et al., 2018).

Sound waves or energy having frequency beyond 20 kHz, inaudible to human beings, called as ultrasonic waves. These waves are responsible for producing vibration in the suspension leading the formation of cavitation bubbles of vapor. The formation of microbubbles and its growth and collapse called cavitation effect, it occurs only due to more ultrasonic energy (e.g., 1 W/cm³ for water) than molecular attractive forces. Microbubbles collapse during wave compression generating high temperature and pressure spot on the biomass disrupting the outer structure. This cavitation delivers physical effects on the substrate due to shear forces to the surface of the substrate leading its structure lysis (Nakashima et al., 2016; Rodriguez et al., 2017; Wang et al., 2017).

Depending on the ultrasonication condition, effective physical disintegration can be achieved augmenting AD yield. It was also proven to be effective and versatile in comparison with other pretreatment such as acid, base thermal and bacterial for fat-rich solid substrates especially waste coming from meat processing (Cesaro and Belgiorno, 2014). Pretreatment parameters should require to be near at room temperature and pressure, mild severity with significant delignification. Ultrasonication exclusiveness is due to its parameters ranges near to atmospheric conditions i.e., room temperature, atmospheric pressure, no chemicals required (Nakashima et al., 2016; Xiong et al., 2017). Factors influencing cavitation effect are vibration frequency, ultrasound power (or amplitude), temperature and viscosity of liquid (solvent), surface tension, and time duration (Clark and Nujjoo, 2000; Bussemaker and Zhang, 2013; Rasapoor et al., 2016).

Deepanraj et al. (2017) found that the ultrasonication (20 kHz, 130 W, 30 min) as more effective pretreatment than autoclave and microwave for biogas production from food waste. Castrillón et al. (2011) analyzed ultrasound (20 kHz, 100 W, 4 min) pretreatment and co-digestion of cattle manure with 4% glycerin both showing 121 and 400% increment, respectively on biogas production in mesophilic and thermophilic condition. Cesaro et al. (2012) sonicated mixture of organic solid waste and sewage sludge for 30 and 60 min duration at different energy densities (0.1, 0.2 and 0.4 W/ml). Twenty-four percent enhanced biogas from the sonicated mixture due to enhanced solubilization and enhanced biodegradability of organic matter enhanced during the AD. Cho et al. (2013) conducted sonication for microalgae at different power amplitude for 15 and 30 min duration without temperature control on microalgae Scenedesmus, and methane increment was around 14–75%. Effect of ultrasonic power density and sonication time was analyzed by performing ultrasonic pretreatment of OFMSW on biogas production and proving low power density and higher sonication time give better results than high power density and low sonication time due to simultaneously temperature increment with respect to time making more effective. Effect of sonication on municipal sludge has widely investigated, however, there is an inadequate study on different organic residues (Cesaro et al., 2014; Rasapoor et al., 2016; Zeynali et al., 2017).

As such this study investigates the feasibility and potential of energy production from AcoD of multi feed-stocks from different waste sectors i.e., agricultural, agro-industrial, and municipal solid waste, through BMP tests to alleviate the energy as well as waste management problem. Also exploring the ultrasonic pretreatment for organic residues from above-mentioned sectors at controlled temperature condition and to validate experimental pretreatment results with two classical kinetic models which can be used to describe and evaluate the batch BMP test for AD process.

A BMP test for biogas production was performed in triplicates of each 6 different substrates, with control bottles i.e., negative control having inoculum alone, and positive control having cellulose as substrates. Subsequently, total 24 bottles flushed with nitrogen and closed with a rubber cap, and transferred to the shaker (REMI CIS 24, India) for incubation (37 °C, 90 rpm, 30 days) to figure out best agro-industrial substrate for co-digestion. 1.55 g substrate VS was added to diluted inoculum bottles (610 ml) having a working volume of 400 ml (Vivekanand et al., 2013; Paritosh et al., 2018).

Pressure measurement in head space of each batch reactors was made periodically through Digital pressure meter (TESTO model 512, Germany, shown in Figure 3A) for biogas volume measurement. After this, biogas was released, reducing head space pressure to atmospheric pressure. The ideal gas law was used for calculating the biogas volume in the headspace volume of the batch reactors. Biogas production from inoculum control bottles is subtracted from biogas production by each substrate (Donoso-Bravo et al., 2010; Vivekanand et al., 2018).

Ten millimeters gas-tight glass syringe used for sampling of biogas for methane composition analysis. Methane composition was analyzed by Gas chromatography (Thermo SCIENTIFIC Trace 1110) equipped with Porapak column and Thermal Conductivity Detector (TCD) where helium gas was used as carrier gas (Zhen et al., 2016).

Five ratios were selected based on nutritional content (C/N ratio) of individual substrates and mixed on VS basis (Mata-Alvarez et al., 2014; Pellera and Gidarakos, 2017; Vivekanand et al., 2018). BMP test of these mixtures was performed for an incubation period of 60 days at 37°C with shaking of 90 rpm.

Each selected substrate suspension (10 g in 200 mL of water) in a beaker (500 mL) was sonicated (Wang et al., 2017; Xiong et al., 2017). Ultrasonic pretreatment was carried out in a probe-type sonicator (Ultrasonic Processor-sonicator EI-250UP, Electrosonic Industries, Mumbai, India) equipped with a probe of 15 mm diameter (shown in Figure 3B) operating at frequency 22 kHz and power of 250 W.

Ultrasonication pretreatment was performed by immersing probe to a half depth of suspension. Here, only pretreatment time is accounted as pretreatment variable i.e., for 30, 60, and 90 min (Nakashima et al., 2016). The temperature of suspension was maintained constant at 32 ± 1°C by recirculating cold water in the provided water bath and manual stirring is performed to have uniform pretreatment effect on substrates. Here, the project aims to investigate the influence of ultrasonic pretreatment duration without temperature effect on methane production.

Afterward, BMP test was performed to evaluate the effect of ultrasonic pretreatment on methane production from different substrates.

Proximate analysis i.e. Moisture content, Total solids (TS) and VS of the inoculum and substrates were determined by standard methods from American Public Health Association (APHA, 1999). Ultimate analysis i.e. carbon, hydrogen and nitrogen content were determined by CHNS analyser (Thermo Finnigan, FLASH EA 1112 series, Italy). pH of batch bottles was measured by a pH meter (LMPH 10, Labman Scientific Instruments Pvt. Ltd. India) in the beginning and end of the BMP test. The proximate and ultimate results of selected substrates are shown in Table 1.

The different kinetics models have been used in previous studies to determine the methane yield by fitting the measured and predicted methane yield. Cone model and Exponential kinetic (or First order kinetic) model were chosen to fit the methane yield. Principal kinetic patterns (digestion mechanism of the organic substrate) of methane production from BMP test can be described through these models. Both models have an assumption of methane production in the batch test is proportional to methanogenic bacteria growth rate. These two models were used to compare and understand the kinetics of methane production from AD (El-Mashad, 2013; Zhen et al., 2016; Syaichurrozi, 2018).

Hydrolysis process as rate limiting step is the basis for using Exponential kinetic model describing the AD assuming no accumulation of intermediary elements (Veeken and Hamelers, 1999; Li K. et al., 2015). And, cone model gives better fit with methane production than other models reported by El-Mashad (2013), Li K. et al. (2015) and Paritosh et al. (2017). The equations of Cone model (Equation 2), and Exponential model (Equation 3) were shown below:

B = Accumulative methane yield (ml/g VS), P = Final methane yield (ml/g VS), k = hydrolysis rate constant (1/day), n = shape factor, t = digestion time (day) for t ≥0. With the help of non-linear least square fitting of accumulative methane yield, parameter “k” and “n” were estimated (Syaichurrozi, 2018).

The coefficient of determination (R²) and Root mean square error (RMSE) were used as indicators for model fitness, and to compare the accuracy of both models. R² is an index for the goodness of fit, and RMSE gives standard deviation between measured and predicted values, the better fit model shows low RMSE (Li K. et al., 2015). RMSE can be calculated through following formula:

Where, Y_p and Y_m are predicted and measured methane yield (ml/g VS), respectively, and n is a number of measurements performed (El-Mashad, 2013).

The results of I-BMP test of individual substrates showing biogas production are shown in Figure 4 of different agro-industrial waste for digestion time of 30 days. Accumulated biogas production in 30 days of digestion period from raw Groundnut shell, Mung bean husk, Guar husk, and Rice bran are also defined in Table 2. Mung bean husk showed maximum potential of biogas among other agro-industrial waste (i.e., groundnut shell, guar husk, and rice bran) therefore it was selected as co-substrate with wheat straw and OFMSW for the multi feed-stock AD. Biogas production from rice bran was low due to its high protein content making unstable AD process (Favaro et al., 2017).

The II-BMP test of selected substrate i.e., OFMSW, WS, and MBH, and anaerobic co-digestion of five different mixing ratio shown in Table 2 of these substrates were performed for a retention time of 60 days. Accumulative biogas production in 60 days from above-mentioned substrates and mixtures are shown in Figure 5. Biogas production of OFMSW, WS, and MBH in mono-digestion mode were 397, 431, and 468 ml/g VS, respectively. AcoD having mixture M3 having a composition of OFMSW, WS, and MBH at 42, 7.5, and 50.5%, respectively ratios produced maximum biogas production in comparison with other mixing ratios.

Comparison of biogas and methane production of substrates in mono-digestion and co-digestion mode with total methane fraction are shown in Table 2. Methane produced and its total fraction in biogas from WS, OFMSW, and MBH are 225 (52%), 203 (51%), and 267 (57%) ml/g VS. WS cellulosic enzymatic hydrolysis is the slow due presence of hemicellulose and lignin can be seen from accumulative biogas production curve in Figure 5 (Zheng et al., 2018). Methane production through AcoD of mixtures were 256 (M1), 226 (M2), 275 (M3), 280 (M4), and 84 (M5) ml/g VS. Mixture M4 showed maximum methane production with 280 ml/g VS with methane fraction of 59%, whereas mixture M5 has minimum methane production 84 ml/g VS, due to the nutritional imbalance i.e., high C/N ratio of 52 which led to the accumulation of VFA inhibiting the methanogens.

The enhanced methane yield from mixtures M1-M4 showed the good synergistic effect of co-digestion. For better methane yield mixture of such substrates should have a composition of OFMSW and MBH in the range of 25–42%, and 50–70%, respectively. The synergistic effect of co-digestion can be strengthened by optimizing the above-mentioned range of specific substrate fractions. The mixture M4 methane yield showed 37, 20, and 4% higher methane yield in comparison with mono-digestion of OFMSW, WS, and MBH.

Untreated and pretreated substrates were digested anaerobically at mesophilic conditions, for which III-BMP test was performed whose results were shown in Table 2. The effect of pretreatment was evaluated by analyzing following sections of respective substrates.

The degradation kinetics of selected substrates were described by using Cone and Exponential kinetic models for III-BMP test. Table 3 shows the experimental methane yield (ml/g VS) from BMP-III test and predicted methane yield (ml/g VS) for respective kinetic models with relevant kinetic parameters. For both models, hydrolysis rate constant “k” increasing with pretreatment duration of respective substrate samples with few exceptions. Hydrolysis rate constant “k” reflects biodegradability of substrates. High “k” refers to the faster rate of degradation of organic substrate, enhancing methane production (Veeken and Hamelers, 1999; Zhen et al., 2015; Mao et al., 2017).

The studied model results of OFMSW shows improvement in measured methane and maximum methane potential was independent of “k,” that rise in “k” doesn't convey high methane production. This inference was similar to Zhen et al. (2016) and Keymer et al. (2013). In case of cone model, “k” ranges between 0.21 and 0.24 day⁻¹ for MBH, 0.064–0.083 day⁻¹ for WS, and 0.135–0.16 day⁻¹ for OFMSW, in case of exponential kinetic model “k” ranges 0.13–0.15 day⁻¹ for MBH, 0.046–0.06 day⁻¹ for WS, and 0.086–0.097 day⁻¹ for OFMSW. The “k” values are comparatively lower for lignocellulosic biomass, therefore lignin content is higher in WS, than substrate MBH, and OFMSW, as it contains green waste.

The calculated RMSE and R² values for both models are shown in Table 3, their lower values confirming the model's good agreement with experimental results. RMSE values vary in between 4.4–12.1 for Cone model and 3.9–19.2 for the Exponential model. This reflects Cone model with a lower range of RMSE, is more accurate than the Exponential kinetic model. The cone model (R²: 0.9592–0.9929) and Exponential kinetic model (R²: 0.9723–0.9973) showed a good fit to predicted and measured data of different untreated and pretreated substrates in mono-digestion. Figure 15A,B shows relation between predicted methane yield against measured values for both Cone and Exponential kinetic model through R².

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.