Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.The authors acknowledge the financial support of INRA (the French National Institute for Agricultural Research), the French National Research Agency (ANR) for the "Phycover" project (project ANR-14-CE04-0011) and ADEME for Inter-laboratory assay financial support.Jimenez, J.; Latrille, E.; Harmand, J.; Robles Martínez, Á.; Ferrer Polo, J.; Gaida, D.; Wolf, C.... (2015). Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Reviews in Environmental Science and Biotechnology. 14(4):615-648. doi:10.1007/s11157-015-9382-6S615648144Aceves-Lara CA, Latrille E, Steyer JP (2010) Optimal control of hydrogen production in a continuous anaerobic fermentation bioreactor. 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Investigation of Bio-hydrogen and Bio-methane Production From Thin Stillage

An evaluation of single-stage and two-stage anaerobic digestion processes for biomethane and biohydrogen production using thin stillage was performed to assess the viability of biohydrogen production from thin stillage and the impact of separating the acidogenic and methanogenic stages on anaerobic digestion with hydrogen production in the first stage. A comparative evaluation of anaerobic digester sludge (ADS) and acclimatized anaerobic digester sludge (AADS) for biohydrogen production was performed at various S°/X° ratios. The optimum range of S°/X° ratio for hydrogen production was found to be 1 to 2 gCOD/gVSS using conventional ADS and 3 to 6 gCOD/gVSS using AADS. Maximum methane yields of 0.33 L CH4/gCODadded and 0.26 L CH4/gCODadded were achieved in the two-stage and the single-stage processes, respectively. An artificial neural network model was developed to estimate the hydrogen production profile with time in batch studies and successfully predicted it with a correlation coefficient of 0.965

Simulation, optimization and instrumentation of agricultural biogas plants

During the last two decades, the production of renewable energy by anaerobic digestion (AD) in biogas plants has become increasingly popular due to its applicability to a great variety of organic material from energy crops and animal waste to the organic fraction of Municipal Solid Waste (MSW), and to the relative simplicity of AD plant designs. Thus, a whole new biogas market emerged in Europe, which is strongly supported by European and national funding and remuneration schemes. Nevertheless, stable and efficient operation and control of biogas plants can be challenging, due to the high complexity of the biochemical AD process, varying substrate quality and a lack of reliable online instrumentation. In addition, governmental support for biogas plants will decrease in the long run and the substrate market will become highly competitive. The principal aim of the research presented in this thesis is to achieve a substantial improvement in the operation of biogas plants. At first, a methodology for substrate inflow optimization of full-scale biogas plants is developed based on commonly measured process variables and using dynamic simulation models as well as computational intelligence (CI) methods. This methodology which is appliquable to a broad range of different biogas plants is then followed by an evaluation of existing online instrumentation for biogas plants and the development of a novel UV/vis spectroscopic online measurement system for volatile fatty acids. This new measurement system, which uses powerful machine learning techniques, provides a substantial improvement in online process monitoring for biogas plants. The methodologies developed and results achieved in the areas of simulation and optimization were validated at a full-scale agricultural biogas plant showing that global optimization of the substrate inflow based on dynamic simulation models is able to improve the yearly profit of a biogas plant by up to 70%. Furthermore, the validation of the newly developed online measurement for VFA concentration at an industrial biogas plant showed that a measurement accuracy of 88% is possible using UV/vis spectroscopic probes

ESTIMATION OF GREENHOUSE GAS AND ODOUR EMISSIONS FROM COLD REGION MUNICIPAL BIOLOGICAL NUTRIENT REMOVAL WASTEWATER TREATMENT PROCESSES

Rising human populations and ever-increasing demand for potable water result in increased municipal wastewater production. The collection, treatment, and management of municipal wastewaters include energy-intensive processes leading to the generation and emission of greenhouse, potentially toxic, and odorous gases. The main goal of this thesis was to advance knowledge of greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and smelly compound (including ammonia, NH3; and hydrogen sulphide, H2S) emissions from typical municipal wastewater treatment plants (MWTPs) to accurately describe their emission rate estimates (EREs) using operating parameters. This research included laboratory and field assessments of greenhouse gas (GHG) and odour emissions in conjunction with monitored operating parameters. Laboratory-scale reactors simulating open-to-air treatment processes including primary and secondary clarifiers and anaerobic, anoxic, and aerobic reactors, were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes in winter and summer seasons. The Saskatoon Wastewater Treatment plan (SWTP) is a state-of-the-art biological nutrient removal (BNR) type MWTP and a Class IV treatment facility in Canada which was selected as a case study given its highly variable seasonal temperatures from −40 °C to 30 °C and its geographic location near the University of Saskatchewan. The experimental results were then used to develop a variety of novel machine learning models describing gas EREs with further optimization of operating parameters using genetic algorithm (GA). Studied machine learning models were artificial data generation algorithms (including generative adversarial network, GAN) and data-driven models (including artificial neural network, ANN; adaptive network-based fuzzy inference systems, ANFIS; and linear/non-linear regression models). To my knowledge, this is the first application of GAN used for MWTP modelling purposes. Results indicated that anaerobic digestion EREs averagely reached 4,443 kg CH4/d, 9,145 kg CO2/d, and 59.7 kg H2S/d. In contrast, GHG and odour ERE variabilities given ambient temperature changes were more noticeable for open-to-air treatment processes such that the winter EREs were 45,129 kg CO2/d, 21.9 kg CH4/d, 3.20 kg N2O/d, and insignificant for H2S and NH3. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively, and still insignificant NH3 emissions. However, the CO2 EREs were reduced to 37,794 kg CO2/d, and interestingly, NH3 emissions were still negligible. Overall, the aerobic reactor was the dominant source of GHG emissions for both seasons, and changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs. The integration of field monitoring data with data-driven models showed that the ANN, ANFIS, and regression models provided reasonable EREs using: (1) volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate for anaerobic digestion biogas generations; and (2) hydraulic retention time, temperature, total organic carbon, dissolved oxygen, phosphate, and nitrogen concentrations for aerobic GHG emissions. However, when both model accuracy and uncertainty were considered there appears to be a compromise between these parameters with no model having simultaneously both high accuracy and low uncertainty. Additionally, and interestingly, virtual data augmentation using GAN was found to be a valuable resource in supplementation of limited data for improved modelling outcomes. GA was also coupled with the data-driven models to determine optimal operating parameters resulting in either GHG emission maximization given biogas could be beneficial for energy generation or GHG emission minimization given the aerobic reactor is an open-to-air process that can impact nearby residential neighbourhood air quality. The current study provides a hybrid methodology of mathematical modelling and experiments that can be used to accurately estimate and optimize the GHG and odour EREs from other MWTPs in Canada and worldwide

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling

Investigating renewable energy systems using artifcial intelligence techniques

This research investigated applying Artificial Intelegence (AI) and Machine Learning (ML) to renewable energy through three studies. The first study characterized and mapped the recent research landscape in the field of AI applications for various renewable energy systems using Natural Language Prcoessing (NLP) and ML models. It considered published documetns at Scopus database in the period (2000-2021). The second study built hybrid Catboost-CNN-LSTM architecture pipeline to predict an industrial-scale biogas plant’s daily biogas production and investigate the feedstock components importance on it. The third study investigated prediciting biogas yield of various subtrates and the significance of each organic component (carbohydrates, proteins, fats/lipids, and legnin) in biogas production using hybrid VAE-XGboost model. The first study showed seven main metatopics and ascent of "deep learning (DL)" as a prominent methodology led to an increase in intricate subjects, including the optimization of power costs and the prediction of wind patterns. Also, a growing utilization of DL approaches for the analysis of renewable energy data, particularly in the context of wind and solar photovoltaic systems. The research themes and trends observed in the first study signify substantial recent investments in advanced AI learning techniques. The developed Catboost-CNN-LSTM pipeline achived a significant results and presented a superior approach when compared to previous relevant studies by eliminating the requirement for feature engineering, enabling direct prediction of biogas yield without the need for converting it into a classification task. The VAE-XGboost pipeline could ovcercome data limitation in the field and produced significant results. It has shown that the "fats" category is the most influential group on the methane production in biogas plants, however, “proteins” illustrated the lowest impact on biogas production

Exploitation of experimental design methods and mathematical modeling for improving fermentative biohydrogen production processes

Abstract Considering the non-renewable nature of today's energy sources, alternative solutions need to be introduced to successfully fulfill the world's energy demands in the future. Biohydrogen production processes coupled to the treatment of different organic wastes might satisfy the requirements of a renewable and environmentally friendly energy carrier. A major drawback of this bioprocess is the low hydrogen production yield, thus, the optimization of the fermentation conditions is imperative for achieving a hydrogen-based economy. The most widely used optimization strategies refer to the design of experimental methods, by which certain factors are selected and deliberately varied in order to obtain the desired effects. In addition, the optimization process can be further improved through mathematical modeling and simulations. Some kinetic models have been proposed to describe the progress of substrate degradation and microbial growth coupled with hydrogen production and some soluble metabolite formation in a batch fermentationbased hydrogen production process. This review attempts to summarize the experimental design methods as well as the kinetic models and simulations that were used to investigate the effects of various factors on fermentative hydrogen production processes and to discuss the advantages and limitations of these optimization approaches