In response to strong growth in energy intensive wastewater treatment, public agencies and industry began to explore and implement measures to ensure achievement of the targets indicated in the 2020 Climate and Energy Package. However, in the absence of fundamental and globally recognized approach evaluating wastewater treatment plant (WWTP) energy performance, these policies could be economically wasteful.This paper gives an overview of the literature of WWTP energy-use performance and of the state of the art methods for energy benchmarking. The literature review revealed three main benchmarking approaches: normalization, statistical techniques and programming techniques, and advantages and disadvantages were identified for each one. While these methods can be used for comparison, the diagnosis of the energy performance remains an unsolved issue. Besides, a large dataset of WWTP energy consumption data, together with the methods for synthesizing the information, are presented and discussed. It was found that no single key performance indicators (KPIs) used to characterize the energy performance could be used universally. The assessment of a large data sample provided some evidence about the effect of the plant size, dilution factor and flowrate. The technology choice, plant layout and country of location were seen as important elements that contributed to the large variability observed.

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The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative controi and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model N0.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%.This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.

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The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

The optimization of agricultural and industrial biogas plants with respect to external influences and various process disturbances is essential for efficient plant operation. The fact that most biogas plants are manually operated because of a lack of online-measurements and limited knowledge about the anaerobic digestion process makes it necessary to develop new optimization and control strategies. However, the optimization and control of such plants is a challenging problem due to the underlying highly nonlinear and complex digestion processes. One approach to address this challenge is to exploit the flexibility and power of computational intelligence (CI) methods such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). The use of CI methods in conjunction with a validated plant simulation model, based on the Anaerobic Digestion Model No. 1, allows optimization of the substrate feed mix, a key factor in stable and efficient biogas production. Results show that an improvement of up to 20% in biogas production and substrate reduction can be achieved when compared to conventional manual operation.Zusammenfassung Die Optimierung landwirtschaftlicher und industrieller Biogasanlagen kompensiert den Einfluss von internen und externen Prozessstörungen und ermöglicht einen effizienten Anlagenbetrieb. Die meisten Biogasanlagen werden heute noch aufgrund von fehlender Online-Messtechnik und wegen begrenztem Fachwissen über den anaeroben Faulungsprozess von Hand gefahren. Der Einsatz neuer Optimierungs-und Regelungsstrategien eröffnet dem Betreiber wertvolle und ertragssteigernde Perspektiven. Allerdings ist die Optimierung und Regelung solcher Anlagen wegen der hochgradig nichtlinearen und komplexen Faulungsprozesse eine besondere Herausforderung. Die Flexibilität und Intelligenz von Computational Intelligence (CI) Methoden, wie z. B. Genetischen Algorithmen (GA) und der Particle Swarm Optimization (PSO) qualifizieren diese Verfahren zu geeigneten Lösungswerkzeu-gen. Dies, in Verbindung mit einem validierten Anlagensimulationsmodell, basierend auf dem Anaerobic Digestion Model No. 1, erlaubt die Optimierung der Mischungsverhältnisse bei der Substratzufuhr, welche einer der wichtigsten Schlüssel für eine stabile und effiziente Biogasproduktion ist. Die Ergebnisse zeigen, dass im Vergleich zur konventionellen manuellen Fahrweise eine Verbesserung von bis zu 20% in Bezug auf Biogasproduktion und Substrateinsparung erreicht werden kann.

One of the main problems in operating a wastewater treatment plant is the purification of the excess water from dewatering and pressing of sludge. Because of a high load of organic material and of nitrogen it has to be buffered and treated together with the inflowing wastewater. Different control strategies are discussed. A combination of neural network for predicting outflow values one hour in advance and fuzzy controller for dosing the sludge water are presented. This design allows the construction of a highly non-linear predictive controller adapted to the behaviour of the controlled system with a relatively simple and easy to optimise fuzzy controller. Measurement results of its operation on a municipal wastewater treatment plant of 60,000 inhabitant equivalents are presented and discussed. In several months of operation the system has proved very reliable and robust tool for improving the system's efficiency.

To develop an online probe that is not only sufficiently robust, but also able to measure crucial process variables in biogas plants is a tough challenge. Therefore, a mid‐infrared (MIR) spectroscopic attenuated total reflection (ATR) probe and robust probe fitting were established. A fully automated probe control, calibration after probe cleaning, and analysis of the absorption spectra using machine learning were implemented in order to reduce maintenance of the probe to a minimum. The relevant wavelengths in the MIR spectrum for organic acids, total alkalinity, and ammonium nitrogen concentration were identified. Finally, intensive lab testing was carried out, followed by operation of the complete online measurement system at an industrial biogas plant. In order to improve signal strength and sensitivity, microelectronic mechanical system (MEMS)‐based Fabry‐Pérot interferometers were also investigated.