Demand-driven biogas production by flexible feeding in full-scale – Process stability and flexibility potentials

Evaluating the sustainability of demand oriented biogas supply programs under different flexible hierarchies: A suggested approach based on the triple bottom line principle

In this paper, a decision-making approach based on the triple bottom line concept is presented for evaluating the sustainability of demand-oriented biogas supply (DOBS) programs with regard to their environmental, economic, and social impacts. For the assessment, an indicator system was developed, whose main parameters were quantified by integrating emergy analysis, economic benefit assessment, and a proposed social risk accounting method. The Charnes-Cooper-Wei-Huang (CCWH) model with constrained cone was adopted to calculate the comprehensive sustainability via the synthesis of the economic, environmental, and social indicators, in which eight scenarios were set according to the flexibility hierarchy of biogas supplied for load demand, biogas production mode, and feeding substrates. The evaluation results show that the DOBS scenario of supplying for real-time varying power demand by using straw and livestock manure has the highest sustainability score in our case study. Based on the results, corresponding managerial implications are proposed.

Demand-driven biogas production from Upflow Anaerobic Sludge Blanket (UASB) reactors to balance the power grid

Future energy systems necessitate dispatchable renewable energy to balance electrical grids with high shares of intermittent renewables. Biogas from anaerobic digestion (AD) can generate electricity on-demand. High-rate methanogenic reactors, such as the Upflow Anaerobic Sludge Blanket (UASB), can react quicker to variations in feeding as compared to traditional AD systems. In this study, experimental trials validated the feasibility of operating the UASB in a demand-driven manner. The UASB was operated with leachate produced from a hydrolysis reactor treating grass silage. The UASB demonstrated a high degree of flexibility in responding to variable feeding regimes. The intra-day biogas production rate could be increased by up to 123% under 4 hours in demand-driven operation, without significant deterioration in performance. A model based on kinetic analysis was developed to help align demand-driven operation with the grid. The findings suggest significant opportunities for UASBs to provide positive and negative balance to the electricity grid.

Prequalification of flotation sludge for a sustainable increase in biogas production and in regard of demand-driven feeding strategy

Co-substrates can increase gas production in a digester significantly. The characteristic properties of substrates, depending on the amounts added, influence the processes in the digester reactor. As a consequence, they can have an impact on the buffer capacity, pH value, C:N ratio, dewaterability of the digested sludge and introduce contaminants to the digester among others. In the future, a discontinuous digester feeding could contribute to the demand-driven energy supply by WRRFs. Due to the increasing instability caused by fluctuating organic load, higher demands are placed on the selection of co-substrates. This study examined to what extent flotation sludge from dairy companies is suitable for a sustainable co-digestion. In addition, it should be evaluated whether flotation sludge is applicable for demand-driven feeding strategies. It was shown that flotation sludge has positive effects on the reactor as well as a significant increase in biogas production and a high degree of degradation of at least 80%. Even at high organic loads pH remained at a high level at around 7.5 due to the high alkalinity of the substrate. Nonetheless, addition of more than 20 w-% flotation sludge lead to a significant decrease of the dewaterability of the digested sludge.

Demand-oriented biogas production to cover residual load of an electricity self-sufficient community using a simple kinetic model

Flexible biogas production can enable demand-oriented energy supply without the need for expensive gas storage expansions, but poses challenges to the stability of the anaerobic digestion (AD) process. In this work, biogas production of laboratory-scale AD of maize silage and sugar beets was optimized to cover the residual load of an electricity self-sufficient community using a simple process model based on first-order kinetics. Experiments show a good agreement between biogas demand, predicted, and measured biogas production. By optimizing biogas conversion schedules based on the measured gas production, a gas storage capacity of 7-8 h was identified for maximum flexibility, which corresponds to typical gas storage sizes at industrial biogas plants in Germany. Various stability indicators were continuously monitored and proved resilient process conditions. These results demonstrate that demand-oriented biogas production using model predictive control is a promising approach to enable existing biogas plants to provide balancing energy.

Stability of the Anaerobic Digestion Process during Switch from Parallel to Serial Operation—A Microbiome Study

Anaerobic digestion is a common procedure of treating sewage sludge at wastewater treatment plants. However, plants differ in terms of the number of reactors and, in case of several reactors, their operation mode. To confirm the flexibility of well adapted, full-scale anaerobic digestion plants, we monitored the physicochemical process conditions of two continuously stirred tank reactors over one hydraulic retention time before and after the operation mode was switched from parallel to serial operation. To investigate changes in the involved microbiota, we applied Illumina amplicon sequencing. The rapid change between operation modes did not affect the process performance. In both parallel and serial operation mode, we detected a highly diverse microbial community, in which Bacteroidetes, Firmicutes, Proteobacteria and Claocimonetes were high in relative abundance. While a prominent core microbiome was maintained in both configurations, changes in the involved microbiota were evident at a lower taxonomical level comparing both reactors and operation modes. The most prominent methanogenic Euryarchaeota detected were Methanosaeta and cand. Methanofastidiosum. Volatile fatty acids were degraded immediately in both reactors, suggesting that the second reactor could be used to produce methane on demand, by inserting easily degradable substrates.

Model Predictive Control: Demand-Orientated, Load-Flexible, Full-Scale Biogas Production

Biogas plants have the great advantage that they produce electricity according to demand and can thus compensate for fluctuating production from weather-dependent sources such as wind power and photovoltaics. A prerequisite for flexible biogas plant operation is a suitable feeding strategy for an adjusted conversion of biomass into biogas. This research work is the first to demonstrate a practical, integrated model predictive control (MPC) for load-flexible, demand-orientated biogas production and the results show promising options for practical application on almost all full-scale biogas plants with no or only minor adjustments to the standardly existing measurement technology. Over an experimental period of 36 days, the biogas production of a full-scale plant was adjusted to the predicted electricity demand of a “real-world laboratory”. Results with a mean absolute percentage error (MAPE) of less than 20% when comparing biogas demand and production were consistently obtained.

Anaerobic digestion of untreated and treated process water from the hydrothermal carbonisation of spent coffee grounds

This study investigates the long-term performance of the mesophilic (35 °C) anaerobic mono-digestion of process waters (PW) from the hydrothermal carbonisation (HTC) of spent coffee grounds. At an organic loading rate (OLR) of 0.4 g_COD L^-1 d^-1, initial instability was seen, but after 40 days and supplementary alkalinity, the digestion stabilised with the chemical oxygen demand (COD) in the untreated PW degraded with 37.8-64.6% efficiency and the yield of methane at 0.16 L g_COD^-1. An increase in OLR to 0.8 g_COD L^-1 d^-1 caused a collapse in biogas production, and resulted in severe instability in the reactor, characterised by falling pH and an increasing volatile fatty acid concentration. Comparatively, the digestion of a treated PW (concentrated in nanofiltration and reverse osmosis after removal of the fouling fraction), at OLR between 0.4 and 0.8 g_COD L^-1 d^-1, was stable over the entire 117 days of treated PW addition, yielded methane at 0.21 L g_COD^-1 and the COD was degraded with an average efficiency of 93.5% - the highest efficiency the authors have seen for HTC PW. Further anaerobic digestion of untreated PW at an average OLR of 0.95 g_COD L^-1 d^-1 was stable for 38 days, with an average COD degradation of 69.6%, and methane production between 0.15 and 0.19 L g_COD^-1. The digestion of treated PW produced significantly higher COD degradation and methane yield than untreated PW, which is likely to be related to the removal of refractory and inhibitory organic material in the post-HTC treatment by adsorption of hydrophobic material.

Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process

Process optimization is no longer an option for processes, but an obligation to survive in the market in any industry. This argument also applies to anaerobic digestion in biogas plants. The contribution of biogas plants to renewable energy can be increased through more productive systems with less waste, which brings the common goal of minimizing costs and maximizing yields in processes. With the help of data science and predictive analytics, it is possible to take conventional process optimization and operational excellence methods, such as statistical process control and Six Sigma, to the next level. The more advanced the process optimization aspect, the more transparent and responsive the systems. In this study, seven different machine learning algorithms—linear regression, logistic regression, K-NN, decision trees, random forest, support vector machine (SVM) and XGBoost—were compared with laboratory results to define and predict the possible impacts of wide range temperature fluctuations on process stability. SVM provided the best accuracy with 0.93 according to the metric precision of the models calculated using the confusion matrix.

Effect of a variable organic loading rate on process kinetics and volatile solids destruction in synthetic food waste-fed anaerobic digesters

With the increasing installation of weather-dependent renewable sources such as solar and wind power, the ability to produce electricity on demand to balance any shortfall in supply is becoming more important. Anaerobic digestion is a low-carbon energy source with the potential to be flexible to meet this need. An investigation was conducted into the response of two laboratory-scale anaerobic digesters at loading rate of 2.5 gVS L^-1 day^-1 over five months using a synthetic food waste as a substrate. One digester was consistently fed at the same rate, whereas the other digester was fed with periods of varying organic loading rate, from 0.1 to 7 gVS L^-1 day^-1, using a feed pattern derived from a record of restaurant food waste. The digester that had been fed at a variable rate showed a pronounced increase in biogas production after feed events and a 9.6% higher VS breakdown than the steady-feed digester (81% compared to 74%), with no effect on digester stability, volatile fatty acid concentration, overall biogas output or biogas quality. These findings support and encourage the use of variable-rate feeding to balance the electricity demand.

Demand-oriented biogas production and biogas storage in digestate by flexibly feeding a full-scale biogas plant

This work studied the demand-oriented biogas production and the biogas storage in digestate by flexibly feeding a full-scale research biogas plant. The investigated continuous stirred tank reactor (CSTR) was equipped with a fast-moving submersible motor mixer and a slow-moving inclined shaft agitator. A model for the biogas storage in digestate was introduced and tested in full scale using temporally highly resolved volume flow measurements. An increase in mixing time led to a faster biogas production: A two to five hours reduction of the time to reach the maximum biogas production after feeding occurred in our experiments. However, no influence of the rheology and of the mixing regime on the methane yield could be derived from the measurements. Further, a 30% reduction of the stored biogas in the digestate occurred when the viscosity was lowered by 66%. This knowledge can be used to enhance the existing biogas formation models.

Empirical Validation of a Biogas Plant Simulation Model and Analysis of Biogas Upgrading Potentials

Biogas plants may support the transformation towards renewable-based and integrated energy systems by providing dispatchable co-generation as well as opportunities for biogas upgrading or power-to-X conversion. In this paper, a simulation model that comprises the main dynamics of the internal processes of a biogas plant is developed. Based on first-order kinetics of the anaerobic digestion process, the biogas production of an input feeding schedule of raw material can be estimated. The output of the plant in terms of electrical and thermal energy is validated against empirical data from a 3-MW biogas plant on the Danish island of Bornholm. The results show that the model provides an accurate representation of the processes within a biogas plant. The paper further provides insights on the functioning of the biogas plant on Bornholm as well as discusses upgrading potentials of biogas to biomethane at the plant from an energy perspective.

Modeling and Simulation of Biogas Production in Full Scale with Time Series Analysis

Future biogas plants must be able to produce biogas according to demand, which requires proactive feeding management. Therefore, the simulation of biogas production depending on the substrate supply is assumed. Most simulation models are based on the complex Anaerobic Digestion Model No. 1 (ADM1). The ADM1 includes a large number of parameters for all biochemical and physicochemical process steps, which have to be carefully adjusted to represent the conditions of a respective full-scale biogas plant. Due to a deficiency of reliable measurement technology and process monitoring, nearly none of these parameters are available for full-scale plants. The present research investigation shows a simulation model, which is based on the principle of time series analysis and uses only historical data of biogas formation and solid substrate supply, without differentiation of individual substrates. The results of an extensive evaluation of the model over 366 simulations with 48-h horizon show a mean absolute percentage error (MAPE) of 14–18%. The evaluation is based on two different digesters and demonstrated that the model is self-learning and automatically adaptable to the respective application, independent of the substrate’s composition.

Integration of Artificial Intelligence into Biogas Plant Operation

In the biogas plants, organic material is converted to biogas under anaerobic conditions through physical and biochemical processes. From supply of the raw material to the arrival of the products to customers, there are serial processes which should be sufficiently monitored for optimizing the efficiency of the whole process. In particular, the anaerobic digestion process, which consists of sequential complex biological reactions, requires improved monitoring to prevent inhibition. Conventional implemented methods at the biogas plants are not adequate for monitoring the operational parameters and finding the correlation between them. As Artificial Intelligence has been integrated in different areas of life, the integration of it into the biogas production process will be inevitable for the future of the biogas plant operation. This review paper first examines the need for monitoring at the biogas plants with giving details about the process and process monitoring as well. In the following sections, the current situation of implementations of Artificial Intelligence in the biogas plant operation and in the similar industries will be represented. Moreover, considering that all the information gathered from literature and operational needs, an implementation model will be presented.

Flexible feeding in anaerobic digestion - Impact on process stability, performance and microbial community structures

Biogas has the potential to contribute to some of the most urgent issues of the energy transition, including mobility, energy storage, and grid stability. Flexibilization has been discussed as a means to improve the economics of biogas production, ideally restricting the production of electricity to times of strong need. Here the possibility of demand-driven, flexible biogas production is investigated, which saves substrates and storage capacity, while still enabling control over the production of electricity. Effects of different flexible feeding regimes were tested in a continuously operated 200 L reactor. After a period of 300 days under steady conditions (6.4 kg feed m^-3d^-1), varying flexible feeding patterns were applied over the next 700 days. Biogas production, volatile organic acid concentrations, and microbial dynamics were documented. Reduction of feeding resulted in reducing the gas production by up to 80% within a day. By increasing the feed, gas production could rapidly be reinitiated at similar levels as before even after fasting periods of up to 22 days. CH₄-contents of the produced biogas were nearly constant over the investigation period. As a response to the flexible feeding, a reorganization of the microbial community was observed, which came to an end after 800 days and then was no longer affected by further changes in the feeding patterns or the substrate composition. Dominating archaea were of the order Methanosarcinales. During the experiment, representatives from the class Methanosaetaceae replaced representatives from the class Methanosarcinaceae.

Dynamic biogas production from anaerobic digestion of sewage sludge for on-demand electricity generation

The aim of this work was to study the potentials and benefits of dynamic biogas production from Anaerobic Digestion (AD) of sewage sludge. The biogas production rate was aimed to match the flexible demand for electricity generation and so appropriate feeding regimes were calculated and tested in both pilot and demonstration scale. The results demonstrate that flexibilization capability exists for both conventional AD and advanced AD using Thermal Hydrolysis Process (THP) as pre-treatment. Whilst the former provides lower capability, flexible biogas production was achieved by the latter, as it provides a quick response. In all scenarios, the value of the biogas converted into electricity is higher than with a steady operational regime, increasing by 3.6% on average (up to 5.0%) in conventional and by 4.8% on average (up to 7.1%) with THP. The process has proven scalable up to 18 m³ digester capacity in operational conditions like those in full scale.

Beneficial effects of intermittent feedstock management on biogas and methane production

Intermittent supply of easily degradable carbohydrates can be used for on-demand biogas production. The study tested the effects of splitting feeding portions of sugar beet silage (S) on biogas production rates and total yield, respectively and if methane production rates follow those ones of biogas. Four experimental AD reactors were operated for 117 days at organic loading rates of 2.0 kg_VS m^-3 d^-1 and VS ratios of maize silage (M) to S of 3:1. While M was supplied hourly (h₀-h₁₂), reactors differed only regarding the intermittent S supply, provided at once (h₀), twice (h₀, h₁) and three times (h₀, h₁, h₂) per twelve-hour observation period. Biogas and methane production rates rose simultaneously after S supply and lasted depending on S intakes. Biogas and methane yields were significantly increased at S given once and twice per period. Appropriate feedstock management can thus influence production rates and increase biogas and methane yields.

The Possible Roles of Wastewater Treatment Plants in Sector Coupling

The development of a power system based on high shares of renewable energy sources puts high demands on power grids and the remaining controllable power generation plants, load management and the storage of energy. To reach climate protection goals and a significant reduction of CO2, surplus energies from fluctuating renewables have to be used to defossilize not only the power production sector but the mobility, heat and industry sectors as well, which is called sector coupling. In this article, the role of wastewater treatment plants by means of sector coupling is pictured, discussed and evaluated. The results show significant synergies—for example, using electrical surplus energy to produce hydrogen and oxygen with an electrolyzer to use them for long-term storage and enhancing purification processes on the wastewater treatment plant (WWTP). Furthermore, biofuels and storable methane gas can be produced or integrate the WWTP into a local heating network. An interconnection in many fields of different research sectors are given and show that a practical utilization is possible and reasonable for WWTPs to contribute with sustainable energy concepts to defossilization.

Residues from the dairy industry as co-substrate for the flexibilization of digester operation

Water resource recovery facilities (WRRF) can make an important contribution to increase the share of renewable energies in Germany. In this context, it is important to utilize unused digester capacities on WRRF. In addition, a demand-orientated biogas production could synchronize electricity demand and electricity generation and improve the overall energetic balance of the WRRF. As part of the project "Water Resource Recovery Facilities in interaction with the waste and energy industry: A German-Austrian Dialogue - COMITO," the influence of residues from the dairy industry on the digestion process was examined as well as the suitability for the flexibilization of digester gas production. Four reactors were fed with different amounts of flotation sludge from the dairy industry for several months. The difference in the feed resulted in organic loading (OLR) rates between 3.2 kg COD/(m³  day) and 6 kg COD/(m³  day). The reactors were fed with a daily shock load. The investigations showed that volumetric loads up to 4.4 kg COD/(m³  day) did not lead to an accumulation of organic acids. Organic loading rate of 6 kg COD/(m³  day) showed a significant accumulation of organic acids higher than 2,500 mg/L oHAc. Nevertheless, the reactor could be operated with a degradation rate of 71% with a corresponding biogas yield with a methane content of 71%. With increasing flotation sludge content, a higher concentration in ammonium of up to 2.000 mg/L NH₄ -N could be detected in the effluent of the digester. Despite higher phosphorus concentration in the flotation sludge, the concentration of PO₄ -P remained constant for all reactors fluctuating between 20 and 40 mg/L PO₄ -P. Dewatering worsened significantly with increasing levels of flotation sludge. PRACTITIONER POINTS: Main purpose of the research is to flexibilize digester operation on WRRF using flotation sludges from the dairy industry. Flexibilization of the digester using flotation sludge is possible up to an organic load of 6 kg COD/(m³  day). Higher NH₄ -N concentration in the effluent of the digester must be accepted when using higher amounts of flotation sludge. Phosphate concentration in the effluent of the digester remained on a low level despite higher phosphorus content in the flotation sludge. High levels of organic acids (mainly acetic acid) can be tolerated and can be recovered within a short time after reducing the load.

Experiments and Modeling for Flexible Biogas Production by Co-Digestion of Food Waste and Sewage Sludge

This paper explores the feasibility of flexible biogas production by co-digestion of food waste and sewage sludge based on experiments and mathematical modeling. First, laboratory-scale experiments were carried out in variable operating conditions in terms of organic loading rate and feeding frequency to the digester. It is demonstrated that biogas production can achieve rapid responses to arbitrary feedings through co-digestion, and the stability of the anaerobic digestion process is not affected by the overloading of substrates. Compared with the conventional continuous mode, the required biogas storage capacity in flexible feeding mode can be significantly reduced. The optimum employed feeding organic loading rate (OLR) is identified, and how to adjust the feeding scheme for flexible biogas production is also discussed. Finally, a simplified prediction model for flexible biogas production is proposed and verified by experimental data, which could be conveniently used for demand-oriented control. It is expected that this research could give some theoretical basis for the enhancement of biogas utilization efficiency, thus expanding the applications of bio-energy.

Model-based control for a demand-driven biogas production to cover residual load rises

The development of systems for energy storage and demand-driven energy production will be essential to enable the switch from fossil to renewable energy sources in future. To cover the residual load rises, a rigorous dynamic process model based on the Anaerobic Digestion Model No. 1 (ADM1) was applied to analyse the flexible operation of biogas plants. For this, the model was optimised and an operational concept for a demand-driven energy production was worked out. Different substrates were analysed, both by batch fermentation and Weende analysis with van Soest method, to determine the input data of the model. The lab results show that the substrates have got different degradation kinetics and biogas potentials. Finally, the ADM1 was extended with a feeding algorithm which is based on a PI controller. Essential feeding times and quantities of available substrates were calculated so that a biogas plant can cover a defined energy demand. The results prove that a flexible operation of biogas plants with a feeding strategy is possible.

Utilization of Food and Agricultural Residues for a Flexible Biogas Production: Process Stability and Effects on Needed Biogas Storage Capacities

Biogas plants can contribute to future energy systems’ stability through flexible power generation. To provide power flexibly, a demand-oriented biogas supply is necessary, which may be ensured by applying flexible feeding strategies. In this study, the impacts of applying three different feeding strategies (1x, 3x and 9x feeding per day) on the biogas and methane production and process stability parameters were determined for a biogas plant with a focus on waste treatment. Two feedstocks that differed in (1) high fat and (2) higher carbohydrate content were investigated during semi-continuous fermentation tests. Measurements of the short chain fatty acids concentration, pH value, TVA/TIC ratio and total ammonium and ammonia content along with a molecular biology analysis were conducted to assess the effects on process stability. The results show that flexible biogas production can be obtained without negative impacts on the process performance and that production peaks in biogas and methane can be significantly shifted to another time by changing feeding intervals. Implementing the fermentation tests’ results into a biogas plant simulation model and an assessment of power generation scenarios focusing on peak-time power generation revealed a considerable reduction potential for the needed biogas storage capacity of up to 73.7%.

Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques.

Representative Sampling Implementation in Online VFA/TIC Monitoring for Anaerobic Digestion

This paper describes an automatic sampling system for anaerobic reactors that allows taking representative samples following the guidelines of Gy’s (1998) theory of sampling. Due to the high heterogeneity degree in a digester the sampling errors are larger than the analysis error, making representative sampling a prerequisite for successful process control. In our system, samples are automatically processed, generating a higher density of data and avoiding human error by sample manipulation. The combination of a representative sampling system with a commercial automate titration unit generates a robust online monitoring system for biogas plants. The system was successfully implemented in an operating biogas plant to control a feeding-on-demand biogas system.

The Future Agricultural Biogas Plant in Germany: A Vision

After nearly two decades of subsidized and energy crop-oriented development, agricultural biogas production in Germany is standing at a crossroads. Fundamental challenges need to be met. In this article we sketch a vision of a future agricultural biogas plant that is an integral part of the circular bioeconomy and works mainly on the base of residues. It is flexible with regard to feedstocks, digester operation, microbial communities and biogas output. It is modular in design and its operation is knowledge-based, information-driven and largely automated. It will be competitive with fossil energies and other renewable energies, profitable for farmers and plant operators and favorable for the national economy. In this paper we discuss the required contribution of research to achieve these aims.

Maize cob waste pre-treatments to enhance biogas production through co-anaerobic digestion with OFMSW

In the present work, the enhancement of biogas and methane yields through anaerobic co-digestion of the pre-hydrolised Organic Fraction of Municipal Solid Wastes (hOFMSW) and Maize Cob Wastes (MCW) in a lab-scale thermophilic anaerobic reactor was tested. In order to increase its biodegradability, MCW were submitted to an initial pre-treatment screening phase as follows: (i) microwave (MW) irradiation catalysed by NaOH, (ii) MW catalysed by glycerol in water and alkaline water solutions, (iii) MW catalysed by H₂O₂ with pH of 9.8 and (iv) chemical pre-treatment at room temperature catalysed by H₂O₂ with 4 h reaction time. The pre-treatments cataysed by H₂O₂ were performed with 2% MCW (wMCW/v alkaline water) at ratios of 0.125, 0.25, 0.5 and 1.0 (wH₂O₂/wMCW). The pre-treatment that presented the most favourable balance between sugars, lignin, cellulose and hemicellulose solubilisations, as well as low production of phenolic compound and furfural (inhibitors), was the chemical pre-treatment catalysed by H₂O₂, at room temperature, with a ratio of 0.5 wH₂O₂/wMCW (Pre1). This Pre1 was then optimised testing reaction times of 1, 2 and 3 days at a different pH (11.5) and MCW percentage (10% w/v). The optimised pre-treatment that presented the best results, considering the same criteria defined above, was the one carried out during 3 days, at pH 9.8 and 10% MCW w/v (Pre2). The anaerobic reactor was initially fed with the hOFMSW obtained from the hydrolysis tank of an industrial AD plant. The hOFMSW was than co-digested with MCW submitted to the pre-treatment Pre1. In another assay, hOFMSW was co-digested with MCW submitted pre-treatment Pre 2. The co-digestion of hOFMSW + Pre1 increased the biogas yield by 38.9% and methane yield by 29.7%, when compared to the results obtained with hOFMSW alone. The co-digestion of hOFMSW + Pre2 increased biogas yield by 46.0% and CH₄ yield by 36.3%. In both cases, the methane content obtained in the biogas streams was above 66% v/v. These results show that pre-treatment with H₂O₂, at room temperature, is a promising low cost way to valorize MCW through co-digestion with hOFMSW.