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

A model-based control concept for a demand-driven biogas production

With the expansion of highly fluctuating renewable energies (like wind power and photovoltaics) in the last few years, the intelligent integration of these new energy sources into the German energy system is becoming one of the central challenges. Biogas plants can play a key role in this transition. The present thesis investigates the possibilities, underlying mechanisms and dependencies establishing a flexible biogas production by means of demand-driven feeding. Furthermore, a robust control concept for demand-driven operation has to be developed and demonstrated in full-scale.Mit dem Ausbau von fluktuierenden erneuerbaren Energien (Windkraft, Photovoltaik) und dem voraussichtlichen Weiterschreiten dieser Entwicklung wird die intelligente Integration dieser Energiequellen in das Energiesystem zur zentralen Herausforderung. Biogasanlagen besitzen dabei eine Schlüsselrolle. Die vorliegende Dissertation untersucht die Möglichkeiten, zugrundeliegende Mechanismen und Abhängigkeiten zur Etablierung einer flexiblen Biogasproduktion durch bedarfsgesteuerte Fütterung. Es ist ein robustes Regelungskonzept entwickelt und im großtechnischen Maßstab demonstriert worden

Full-scale digesters: an online model parameter identification strategy

This work presents a new standard in the model, identification, and control of monitoring purposes over anaerobic reactors. One requirement that guarantees a normal controller operation is for the faculty to measure the data needed periodically. Due to its inability to easily obtain the concentrations of acidogenic bacteria and methanogenic archaea periodically using reliable and commercial sensors, this paper presents an algorithm composed of an asymptotic observer (considering the reaction rates are unknown), aiming to estimate these concentrations. This method represents a significant advantage because it is possible to perform a resource-saving strategy using standard measurements, such as pH or alkalinity, to calculate them analytically in natural environments. Additionally, two yield parameters were included in the original anaerobic model two (AM2) to unlock implementations for a wide range of organic substrates. The static parameter identification was improved using a new method called step-ahead optimization. It demonstrates significant improvements fitting the mathematical model to data until a (Formula presented.) increase in efficiency (compared with the traditional optimization method genetic algorithm). After the period of convergence, the state observer evidences a small error with a maximum (Formula presented.) deviation. Finally, numerical simulations demonstrate the structure’s strengths, which constitutes a significant step in paving the way further to implement feasible, cost-effective controls and monitoring systems in the industr

Optimisation-based methodology for the design and operation of sustainable wastewater treatment facilities

The treatment of municipal and industrial wastewaters in conventional wastewater treatment plants (WWTPs) requires a significant amount of energy in order to meet ever more stringent discharge regulations. However, the wastewater treatment industry is undergoing a paradigm shift from a focus on waste-stream treatment and contaminant removal to a proactive interest in energy and resource recovery facilities, driven by both economic and environmental incentives. The main objective of this thesis is the development of a decision-making tool in order to identify improvement opportunities in existing WWTPs and to develop new concepts of sustainable wastewater treatment/recovery facilities. The first part of the thesis presents the application of a model-based methodology based on systematic optimisation for improved understanding of the tight interplay between effluent quality, energy use, and fugitive emissions in existing WWTPs. Plant-wide models are developed and calibrated in an objective to predict the performance of two conventional activated sludge plants owned and operated by Sydney Water, Australia. In the first plant, a simulation-based approach is applied to quantify the effect of key operating variables on the effluent quality, energy use, and fugitive emissions. The results show potential for reduced consumption of energy (up to 10-20%) through operational changes only, without compromising effluent quality. It is also found that nitrate (and hence total nitrogen) discharge could be signficantly reduced from its current level with a small increase in energy consumption. These results are also compared to an upgraded plant with reverse osmosis in terms of energy consumption and greenhouse gas emissions. In the second plant, a systematic model-based optimisation approach is applied to investigate the effect of key discharge constraints on the net power consumption. The results show a potential for reduction of energy (20-25%), without compromising the current effluent quality. The nitrate discharge could be reduced from its current level to less than 15 mg/L with no increase in net power consumption and could be further reduced to <5 mg/L subject to a 18% increase in net power consumption upon the addition of an external carbon source. This improved understanding of the relationship between nutrient removal and energy use for these two plants will feed into discussions with environmental regulators regarding nutrient discharge licensing.The second part of the thesis deals with the application of a systematic, model-based methodology for the development of wastewater treatment/resource recovery systems that are both economically and environmentally sustainable. With the array of available treatment and recovery options growing steadily, a superstructure modeling approach based on rigorous mathematical optimisation provides a natural approach for tackling these problems. The development of reliable, yet simple, performance and cost models is a key issue with this approach in order to allow for a reliable solution based on global optimisation. it is argued that commercial wastewater simulators can be used to derive such models. The superstructure modeling framework is also able to account for wastewater and sludge treatment in an integrated system and to incorporate LCA with multi-objective optimisation to identify the inherent trade-off between multiple economic and environmental objectives. This approach is illustrated with two case studies of resource recovery from industrial and municipal wastewaters. The results establish that the proposed methodology is computationally tractable, thereby supporting its application as a decision support system for selection of promising wastewater treatment/resource recovery systems whose development is worth pursuing. Our analysis also suggests that accounting for LCA considerations early on in the design process may lead to dramatic changes in the configuration of future wastewater treatment/recovery facilities.Open Acces

Biological investigation and predictive modelling of foaming in anaerobic digester

Anaerobic digestion (AD) of waste has been identified as a leading technology for greener renewable energy generation as an alternative to fossil fuel. AD will reduce waste through biochemical processes, converting it to biogas which could be used as a source of renewable energy and the residue bio-solids utilised in enriching the soil. A problem with AD though is with its foaming and the associated biogas loss. Tackling this problem effectively requires identifying and effectively controlling factors that trigger and promote foaming. In this research, laboratory experiments were initially carried out to differentiate foaming causal and exacerbating factors. Then the impact of the identified causal factors (organic loading rate-OLR and volatile fatty acid-VFA) on foaming occurrence were monitored and recorded. Further analysis of foaming and nonfoaming sludge samples by metabolomics techniques confirmed that the OLR and VFA are the prime causes of foaming occurrence in AD. In addition, the metagenomics analysis showed that the phylum bacteroidetes and proteobacteria were found to be predominant with a higher relative abundance of 30% and 29% respectively while the phylum actinobacteria representing the most prominent filamentous foam causing bacteria such as Norcadia amarae and Microthrix Parvicella had a very low and consistent relative abundance of 0.9% indicating that the foaming occurrence in the AD studied was not triggered by the presence of filamentous bacteria. Consequently, data driven models to predict foam formation were developed based on experimental data with inputs (OLR and VFA in the feed) and output (foaming occurrence). The models were extensively validated and assessed based on the mean squared error (MSE), root mean squared error (RMSE), R2 and mean absolute error (MAE). Levenberg Marquadt neural network model proved to be the best model for foaming prediction in AD, with RMSE = 5.49, MSE = 30.19 and R2 = 0.9435. The significance of this study is the development of a parsimonious and effective modelling tool that enable AD operators to proactively avert foaming occurrence, as the two model input variables (OLR and VFA) can be easily adjustable through simple programmable logic controller

Modification and experimental calibration of ADM1 for modelling the anaerobic digestion of solid wastes in demand driven applications

This thesis is an exploration into the modelling of anaerobic digestion (AD) with a focus on its integration into a microgrid for rural electrification. The work investigated the improvement of Anaerobic Digestion Model No 1 (ADM1) in order to better describe the kinetics of biogas production in an AD system with particular focus on substrate characterisation, codigestion and the mechanisms of inhibition. The resulting model was used to investigate the possible role of AD in microgrid systems. A novel biochemical and kinetic fractionation method was developed in order to fully characterise any substrate and produce the required input parameters into the a modified version of ADM1. The method uses a combination of analytical and digestion batch tests and was applied to food waste, green waste, pig manure and oat processing residues. The fractionation method was validated using measurements from semi-continuous laboratory scale digesters, operated with varying substrate combinations and loading rates. The model was able to suitably predict the methane production rate and the typical off-line measurements in AD systems, except during periods of high organic loading rate where biochemical inhibition became an important phenomenon. Possible inhibiting mechanisms were investigated by model based analysis of the experimental data characterised by inhibition, and a possible inhibition mechanism was proposed and integrated in the ADM1 model. Microgrid modelling software HOMER was used alongside the updated version of ADM1 in order to perform a benchmark of various operational and control strategies for the demand-driven operation of an AD system integrated in a microgrid. Different biogas demand profiles were considered. In the case of a biogas demand profile with low variability it was found that simple operational strategies could be used, with limited required biogas storage buffer and without causing process instabilities. With more variable demand profiles, an expert control system was needed in order to reduce the biogas storage requirements and guarantee process stability