Soft sensor development and process control of anaerobic digestion

This thesis focuses on soft sensor development based on fuzzy logic used for real time online monitoring of anaerobic digestion to improve methane output and for robust fermentation. Important process parameter indicators such as pH, biogas production, daily difference in pH and daily difference in biogas production were used to infer alkalinity, a reliable indicator of process stability. Additionally, a fuzzy logic and a rule-based controller were developed and tested with single stage anaerobic digesters operating with cow slurry and cellulose. Alkalinity predictions from the fuzzy logic algorithm were used by both controllers to regulate the organic loading rate that aimed to optimise the biogas process. The predictive performance of a software sensor determining alkalinity that was designed using fuzzy logic and subtractive clustering and was validated against multiple linear regression models that were developed (Partner N° 2, Rothamsted Research 2010) for the same purpose. More accurate alkalinity predictions were achieved by utilizing a fuzzy software sensor designed with less amount of data compared to a multiple linear regression model whose design was based on a larger database. Those models were utilised to control the organic loading rate of a twostage, semi-continuously fed stirred reactor system. Three 5l reactors without support media and three 5l reactors with different support media (burst cell reticulated polyurethane foam coarse, burst cell reticulated polyurethane foam medium and sponge) were operated with cow slurry for a period of seven weeks and twenty weeks respectively. Reactors with support media were proven to be more stable than the reactors without support media but did not exhibit higher gas productivity. Biomass support media were found to influence digester recovery positively by reducing the recovery period. Optimum process parameter ranges were identified for reactors with and without support media. Increased biogas production was found to occur when the loading rates were 3-3.5g VS/l/d and 4-5g VS/l/d respectively. Optimum pH ranges were identified between 7.1-7.3 and 6.9-7.2 for reactors with and without support media respectively, whereas all reactors became unstable at ph<6.9. Alkalinity levels for system stability appeared to be above 3500 mg/l of HCO3 - for reactors without media and 3480 mg/l of HCO3 - for reactors with support media. Biogas production was maximized when alkalinity was 3 between 3500-4500 mg/l of HCO3 - for reactors without support media and 3480- 4300 mg/l of HCO3 - for reactors with support media. Two fuzzy logic models predicting alkalinity based on the operation of the three 5l reactors with support media were developed (FIS I, FIS II). The FIS II design was based on a larger database than FIS I. FIS II performance when applied to the reactor where sponge was used as the support media was characterized by quite good MAE and bias values of 466.53 mg/l of HCO3- and an acceptable value for R2= 0.498. The NMSE was close to 0 with a value of 0.03 and a slightly higher FB= 0.154 than desired. The fuzzy system robustness was tested by adding NaHCO3 to the reactor with the burst cell reticulated polyurethane foam medium and by diluting the reactor where sponge was used as the support media with water. FIS I and FIS II were able to follow the system output closely in the first case, but not in the second. FIS II functionality as an alkalinity predictor was tested through the application on a 28l cylindrical reactor with sponge as the biomass support media treating cow manure. If data that was recorded when severe temperature fluctuations occurred (that highly impact digester performance), are excluded, FIS II performance can be characterized as good by having R2= 0.54 and MAE=Bias= 587 mg/l of HCO3-. Predicted alkalinity values followed observed alkalinity values closely during the days that followed NaHCO3 addition and water dilution. In a second experiment a rulebased and a Mamdani fuzzy logic controller were developed to regulate the organic loading rate based on alkalinity predictions from FIS II. They were tested through the operation of five 6.5l reactors with biomass support media treating cellulose. The performance indices of MAE=763.57 mg/l of HCO3-, Bias= 398.39 mg/l of HCO3-, R2= 0.38 and IA= 0.73 indicate a pretty good correlation between predicted and observed values. However, although both controllers managed to keep alkalinity within the desired levels suggested for stability (>3480 mg/l of HCO3-), the reactors did not reach a stable state suggesting that different loading rates should be applied for biogas systems treating cellulose.New Generation Biogas (NGB

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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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

Modelling, Optimisation and Control of Anaerobic Co-digestion Processes

La digestión anaerobia es un proceso biológico que ocurre espontáneamente en la naturaleza. Sin embargo, el rendimiento de metanización varía mucho dependiendo del tipo de residuo y condiciones ambientales a las que los residuos están expuestos. La tesis “Modelling, Optimisation and Control of Anaerobic Co-digestion Processes” contribuye a la modelización, optimización y control de procesos de co-digestión con el objetivo de mejorar el rendimiento del proceso. Los fundamentos de la digestión anaerobia y co-digestión se presentan en el Capítulo 1, junto con una revisión bibliográfica sobre la modelización del proceso, centrándose principalmente en la descripción y aplicaciones del Anaerobic Digestion Model No. 1 (ADM1), y una revisión de las distintas estrategias de control que están disponibles en la actualidad. El Capítulo 2 describe detalladamente la planta piloto que se utilizó para realizar los ensayos experimentales del trabajo de investigación. En el Capítulo 3 se desarrolla y valida un método generalizado para incorporar diversos sustratos solubles fermentables en un modelo basado en ADM1. Las reacciones de fermentación de sustratos tales como el etanol, no incluidos originalmente en ADM1, se implementan como reacciones de fermentación equivalente de glucosa. Suponiendo que la acidogénesis es el paso más rápido en la digestión anaerobia, una descripción exacta de la estequiometría de la fermentación de sustratos solubles (etanol, glicerol...) y productos (acetato, butirato y propionato) no es necesaria siempre que se cumplan los balances de masa y de electrones, puesto que todos estos ácidos intermedios se convierten rápidamente en acetato, H2 y CO2 en sistemas metanogénicos. El tratamiento de residuos sólidos mediante digestión anaerobia es atractivo por su alto contenido en materia orgánica y al potencial de recuperación de energía. En el caso de sólidos, la etapa de desintegración-hidrólisis es el paso más lento del proceso. El Capítulo 4 presenta un nuevo enfoque para la modelización de las etapas de desintegración e hidrólisis de sustratos sólidos complejos. Éstos se suponen que están compuestos de una fracción fácilmente biodegradable y otra lentamente biodegradable. El modelo propuesto considera una desintegración desacoplada de estas dos fracciones para describir mejor la degradación de los residuos sólidos. La co-digestión puede mejorar el rendimiento de las plantas de biogás en términos de productividad de metano y estabilidad de la operación si se combinan adecuadamente los diferentes co-sustratos. El Capítulo 5 formula y valida un método de optimización basado en programación lineal que calcula la mejor mezcla de alimentación para sistemas de co-digestión, capaz de maximizar la producción de metano a cada velocidad de carga orgánica aplicada. La mezcla resultante está sujeta a un conjunto de restricciones fisicoquímicas, que se definen en base al conocimiento heurístico del proceso. Finalmente, el Capítulo 6 presenta una estrategia de control para co-digestión anaerobia. La mezcla óptima obtenida por programación lineal se alimenta a un digestor operando en continuo y un sistema de diagnosis evalúa el rendimiento del proceso. En función de los resultados de la diagnosis, la acción de control modifica las restricciones aplicadas en el cálculo de la alimentación. Esta acción de control permite calcular una nueva mezcla de sustratos y un nuevo TRH para el próximo período de operación. Como resultado, la estrategia funciona como un controlador en lazo cerrado que optimiza la mezcla de alimentación al digestor y posteriormente evalúa el rendimiento de operación con la mezcla alimentada

Optimisation of energy usage and carbon emissions for an advancedanaerobic digester plant

PhD ThesisIn this thesis Northumbrian Water Limited’s (NWL) Advanced Anaerobic Digester (AAD) plant at Howdon was used to investigate modelling and optimisation opportunities based on energy prices, demands and their new greenhouse gas emissions pledge. It is believed this site is the first in the UK with a mixed operational strategy for biogas and biomethane produced on site: to burn in Combined Heat and Power (CHP) engines to create electricity, burn in Steam Boilers for onsite steam use or inject the biomethane into the national grid - Natural Gas can be imported to make up shortfalls in biomethane if required. Initially, a realistic model for the gas distribution on site was developed using a novel mixed integer linear programming (MILP) approach. Retrospective Optimisation (RO) using historical plant data was performed, with results indicating the plant operated optimally within accepted tolerance 98% of the time. However, improving plant robustness (such as reducing unexpected breakdown incidents) could yield a significant increase in gas revenue of 7.8%. Next, the gas distribution model is developed further as a realistic MILP model for energy and carbon management where operators are provided with a visual daily operational schedule based on varying tariffs. The results indicate that biomethane injection should be maximised for the highest financial gain, with the driving force for optimising the remaining operations being the site electricity demand and whether the electricity purchased from the grid generates carbon emissions, based on the new carbon performance commitment. Using the developed energy and carbon model a sensitivity analysis was performed on electricity tariffs, natural gas prices, the volume of biogas production and the Biomethane Upgrade Plant (BUP) processing limits. The results reinforce the understanding that maximising biomethane injection into the national grid is the most cost-effective operational strategy. Second to this, the optimal operation of the CHP engines is subject to the available excess biogas available after BUP processing and the current daily energy prices. To ensure the site always maintains a positive revenue, operators should ensure that at least 20,000 Nm3 /day of raw biogas can be processed and injected into the national grid. Finally, an investigation into the unique modelling problem regarding the three on site Anaerobic Digesters (ADs) was performed. A key parameter used in the current optimisation model is the amount of biogas that is produced on site each day, however currently an average daily value is used based on historical data. To improve the optimisation, it would be better to provide a more accurate prediction based on current state of the ADs and the expected sludge processing volumes into the ADs. The lack of individual gas flow data for each AD posed an interesting challenge in predicting the total biogas flow produced on site. Multiple linear models of the onsite AD’s were investigated but were not accurate enough to be used on site. A NARX (Nonlinear autoregressive with external input) Neural Network was developed to model all three anaerobic digesters as a single process for the day ahead prediction of biogas production. The resulting optimal NARX model can accurately predict the biogas production on a day-ahead basis over 95% of the time

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

Feasibility of Mainstream Nitrite Oxidizing Bacteria Out-Selection and Anammox Polishing for Enhanced Nitrogen Removal

Short-cut nitrogen removal avoids nitrite oxidation to nitrate by nitrite oxidizing bacteria (NOB) and allows a) reduction of formed nitrite to nitrogen gas via heterotrophic denitrification and/or b) oxidation of remaining ammonia with formed nitrite to nitrogen gas via anaerobic ammonia oxidation (anammox). The precondition for achieving shortcut nitrogen removal is suppression of NOB, which is favored by warm and high ammonia strength conditions found in internally generated ammonia-rich waste streams through anaerobic digestion of waste solids referred to as sidestreams or reject water. The discovery of anammox bacteria in the mid-1990s, which are capable of transforming NH4+ to nitrogen gas utilizing NO2- as a substrate, has made suppression of NOB even more critical for nitrogen removal processes that take advantage of the lower energy and cost requirements of this nitrogen conversion compared to traditional nitrogen removal processes. Deammonification relies on ammonia oxidizing bacteria (AOB) to partially convert NH4+ to NO2- and anammox bacteria (AMX) to convert the remaining NH4+ and NO2- to nitrogen gas. The challenges of retaining slow growing AMX initially limited the expansion of benefits from autotrophic nitrogen removal; however, granular sludge and attached growth systems have proven highly effective in achieving deammonification in sidestream processes. Owing to the benefits that include energy and chemical savings, short-cut nitrogen removal has emerged as a viable technology for sidestream treatment. Consequently, mechanisms of NOB suppression to perform short-cut nitrogen removal are generally quite well understood for sidestream applications, which has allowed for the development of robust process control strategies. To date, the concept of deammonification has successfully been implemented in 100 full-scale treatment facilities treating high ammonia strength waste streams around the world. Due to the success of sidestream short-cut nitrogen removal systems, there is great interest in applying this form of nitrogen removal to mainstream processes. Since the dilute and cold conditions of mainstream are not well-suited for suppression of NOB, short-cut nitrogen removal, in particular deammonification, has yet to be implemented in full-scale. The successful implementation of mainstream deammonification would revolutionize and disrupt the way in which biological nitrogen removal is achieved at wastewater treatment facilities. It represents a paradigm shift for the industry, offering the opportunity for sustainable wastewater treatment, energy neutral or even energy positive facilities and dramatic reductions in treatment costs, which has widespread environmental, economic and societal benefits. This dissertation deals with the pilot-scale investigation of short-cut nitrogen removal in low ammonia strength wastewater with temperatures \u3c25 \u3e°C. An A-B process pilot-scale system was operated over a two year period. The A-stage was a high-rate activated sludge system for carbon removal and the B-stage consisted of an activated sludge system that targeted NOB out-selection which was followed by a fully anoxic anammox MBBR. In this study, by employing a combination of intermittent aeration, high DO (\u3e1.5 mg/L), residual effluent NH4+ (\u3e2 mg/L), and aggressive SRT (\u3c 5 days at 23-25 °C) and HRT (\u3c 4hr), NOB out-selection was achieved in the continuous-flow activated sludge process. The development of novel aeration and SRT control strategies based on advanced instrumentation, control, and automation for achieving NOB out-selection in an activated sludge process and nitrogen polishing in subsequent anammox MBBR was shown. A very fast startup time (less than 2 weeks) for anammox MBBR was achieved by seeding anammox granules obtained from a full-scale, sidestream anammox treatment process. Anammox MBBR proved highly stable during the study and a very high maximum nitrogen conversion rate (\u3e 1 gN/m2/d) was demonstrated. Therefore, this study shows carbon re-direction (potentially for energy production) in a high rate A-stage does not cause carbon limitation in the B-stage for nitrogen removal if control strategies and anammox-based nitrogen polishing is used as investigated in this study