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Pelleted biomass has a low, uniform moisture content and can be handled and stored cheaply and safely. Pellets can be made of industrial waste, food waste, agricultural residues, energy crops, and virgin lumber. Despite their many desirable attributes, they cannot compete with fossil fuel sources because the process of densifying the biomass and the price of the raw materials make pellet production costly. Leaves collected from street sweeping are generally discarded in landfills, but they can potentially be valorized as a biofuel if they are pelleted. However, the lignin content in leaves is not high enough to ensure the physical stability of the pellets, so they break easily during storage and transportation. In this study, the use of eucalyptus kraft lignin as an additive in tree-leaf pellet production was studied. Results showed that when 2% lignin is added the abrasion resistance can be increased to an acceptable value. Pellets with added lignin fulfilled all requirements of European standards for certification except for ash content. However, as the raw material has no cost, this method can add value or contribute to financing continued sweeping and is an example of a circular economy scenario.

Over the last 40 years many different control methodologies for substrate feed control of anaerobic digestion processes have been proposed in order to increase plant efficiency and sustainable long-term energy production. This review shows that although sophisticated controllers exist, full-scale biogas plants are mostly still operated without a closed-loop feed control. No matter which application, such control always has to find a compromise between maximizing economic yield, minimizing the ecological footprint and minimizing the risk of process failure. For anaerobic wastewater treatment, control systems which come close to this ideal, exist, but for agricultural as well as industrial biogas plants such control has not yet been developed and neither been successfully implemented and validated at full-scale. Main challenges are a lack of robust and reliable process monitoring using online instrumentation as well as a conservative industry which is reluctant towards the implementation of fully automated process control strategies.

In North Rhine-Westphalia (Germany) contaminated rainwater by traffic and industry areas could not pass into flowing waters without cleaning. Therefore, many purification tank for rainwater (PTR) have to be built in areas with separate sewers for rainwater and wastewater. The heavy polluted rainwater has to be transmitted over the wastewater treatment plant. The wastewater treatment plant and sewer are constructed only for the past wastewater quantities. Aggravatingly the inflow is added. For this reason, the wastewater treatment plant and sewer are strongly working close by full capacity during rain events. The additional wastewater caused by PBR can lead to an overload of the wastewater treatment plant sewer. This has the consequence of unsettled wastewater in nature. The goal of pollution reduction in flowing waters would be merged thereby. In this case, PTRs have to be controlled centrally. If enough capacity in the wastewater treatment plant and sewer is available each PTR by each PTR could be pumped dry. For this control application a PTR-controller is developed. The controller is tested in simulation tool simba#water. Thereby the operability of the PTR-controller and the discharge for sewer and wastewater treatment plant could be verified.

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 This project is carried out with financial support from the H2020 Coordinated Support Action ENERWATER (grant agreement number 649819): www.enerwater.eu. Although the project's information is considered accurate, no responsibility will be accepted for any subsequent use thereof. The European Community accepts no responsibility or liability whatsoever with regard to the presented material, and the work hereby presented does not anticipate the Commission's future policy in this area. M.M.I. gratefully acknowledges the financial support provided by the People Program (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013 under REA agreement 627475 (GREENCOST). S.L, J.M.L, M.M.I. and A.H. belong to the Galician Competitive Research Group GRC 2013-032, programme co-funded by FEDER, and to the strategic aggregation CRETUS (Centre for Research in Environmental Technologies), AGRUP2015/02 SI

Environmental systems often have a very complex structure. Methods from computational intelligence (CI) that are often inspired by nature can help to improve these systems. On the one hand, CI methods can be used for optimization; on the other hand, they can be used to extract information out of time series recorded from environmental systems. Methods from different fields of computational intelligence are investigated. Among them are supervised and unsupervised machine learning methods used for classification and cluster analysis, respectively. Furthermore, methods from evolutionary computation and multi-agent systems are used to develop control and optimization solutions for environmental processes. In this paper, five applications in the fields of anaerobic digestion, pellet-heating, and wastewater management are studied. Using CI methods, e.g., biogas plant operation or a pellet-heating process can be optimized. Furthermore, important process variables can be obtained from huge measurement datasets that otherwise would be unanalyzed and therefore data cemeteries. The results reveal that using CI methods environmental processes can be improved in a favorable cost-benefit fashion.

The in-line measurement of COD and NH 4 -N in the WWTP inflow is crucial for the timely monitoring of biological wastewater treatment processes and for the development of advanced control strategies for optimized WWTP operation. As a direct measurement of COD and NH 4 -N requires expensive and high maintenance in-line probes or analyzers, an approach estimating COD and NH 4 -N based on standard and spectroscopic in-line inflow measurement systems using Machine Learning Techniques is presented in this paper. The results show that COD estimation using Radom Forest Regression with a normalized MSE of 0.3, which is sufficiently accurate for practical applications, can be achieved using only standard in-line measurements. In the case of NH 4 -N, a good estimation using Partial Least Squares Regression with a normalized MSE of 0.16 is only possible based on a combination of standard and spectroscopic in-line measurements. Furthermore, the comparison of regression and classification methods shows that both methods perform equally well in most cases.

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.

Optimal substrate feed control of biogas plants is a complex and challenging task due to the nonlinearity of the anaerobic digestion process, which produces biogas from biodegradable input material. In this paper a nonlinear model predictive control (NMPC) scheme is applied to optimally control the substrate feed of an agricultural biogas plant. The implemented algorithms are investigated in a simulation study using a validated simulation model of a full-scale biogas plant. Process states are estimated using a recently developed state estimator. Results show that this approach is very feasible providing the plant operator with a gain of 550 € per day compared to previous operation.

The optimization of relaxation and filtration times of submerged microfiltration flat modules in membrane bioreactors used for municipal wastewater treatment is essential for efficient plant operation. However, the optimization and control of such plants and their filtration processes is a challenging problem due to the underlying highly nonlinear and complex processes. This paper presents the use of genetic algorithms for this optimization problem in conjunction with a fully calibrated simulation model, as computational intelligence methods are perfectly suited to the nonconvex multi-objective nature of the optimization problems posed by these complex systems. The simulation model is developed and calibrated using membrane modules from the wastewater simulation software GPS-X based on the Activated Sludge Model No.1 (ASM1). Simulation results have been validated at a technical reference plant. They clearly show that filtration process costs for cleaning and energy can be reduced significantly by intelligent process optimization.

An optimal control of wastewater treatment plants (WWTP) has to account for changes in the bio-chemical state of the bioreactors. As many process variables of a WWTP are not measurable online, the development of an efficient control strategy is one of the greatest challenges in the optimization of WWTP operation. This paper presents an approach, which combines the use of Self-Organizing Maps (SOM) and a clustering algorithm to identify operational patterns in WWTP process data. These patterns provide a basis for the optimization of controller set points that are well suited for the previously identified operation regimes of the plant. The optimization is performed using Genetic Algorithms. This approach was developed, tested and validated on a simulation model based on the Activated Sludge Model No.1 (ASM1). The results of this state-based control indicate that the presented methodology is a promising and useful control strategy that is definitely able to address the distinctive energy and effluent limit challenges faced by WWTP operators.

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 concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes, making a reliable online-measurement system absolutely necessary. This paper introduces a novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods. 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 200nm to 750nm. Advanced pattern recognition methods, like LDA, Generalized Discriminant Analysis (GerDA) and SVM, are then used to map the measured absorption spectra to laboratory measurements of organic acid concentrations. The validation of the approach at a full-scale 1.3MW industrial biogas plant shows that more than 87% of the measured organic acid concentrations can be detected correctly.

The optimization of agricultural biogas plants with respect to external influences and various process disturbances is essential for efficient plant operation. However, the optimization and control of such plants is a challenging problem due the underlying highly nonlinear and complex digestion processes. One approach to addressing this challenge is to exploit the flexibility and power of computational intelligence methods such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). In this paper these methods are used in conjunction with a validated plant simulation model to optimize 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. In addition, a comparison of the performance of GAs and PSO reveals that while both methods can achieve comparable results PSO has faster convergence and hence is preferred for this application.