Eucalyptus Kraft Lignin as an Additive Strongly Enhances the Mechanical Resistance of Tree-Leaf Pellets

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

Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques

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

Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques

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

Development and optimization of a control algorithm for an industrial combustion plant via flame image analysis

Modern industrial biomass combustion plants are regulated by the power and/or combustion control. In this process, the implemented sensors collect the relevant measured data. The aim is to achieve ideal combustion with optimum efficiency and to minimize gas emissions. For this purpose, a group within the research project Metabolon developed new regulatory procedures in order to record the combustion process of a biomass combustion plant using a webcam. The recordings were evaluated automatically and were used for a better monitoring of the process. In addition, the webcam-based method aims, among other things, to provide private homes with a cost-effective variant as an alternative to industrial system solutions

Optimal Control of Biogas Plants using Nonlinear Model Predictive Control

Optimal control of biogas plants is a complex and challenging task due to the nonlinearity of the anaerobic digestion process involved in the conversion of biodegradable input material to biogas (a mixture of the energy carrier methane and carbon dioxide). In this paper a nonlinear model predictive control (NMPC) algorithm is developed to optimally control the substrate feed of the anaerobic digestion process on biogas plants. The implemented algorithm is investigated in a simulation study using a validated simulation model of a full-scale biogas plant with an electrical power of 750 kW, where the control objective is to achieve high biogas production and quality while maintaining stable plant operation. Results are presented demonstrating the feasibility of the proposed approach. The optimal operating state identified by the controller provides an additional return of investment of 650 €/day compared to a nominal operating state. Using the proposed algorithm it will be possible in the near future to optimize full-scale biogas plants using nonlinear model predictive control and therefore to advance the use of anaerobic digestion for eco-friendly energy production

Online-measurement systems for agricultural and industrial AD plants – A review and practice test

Online-measurement systems for AD plants in general are crucial to allow for detailed and comprehensive process monitoring and provide a basis for the development and practical application of process optimisation and control strategies. Nevertheless, the online measurement of key process variables such as Volatile Fatty Acids (VFA) and Total Alkalinity (TA) has proven to be difficult due to extreme process conditions. High Total Solids (TS) concentrations and extraneous material often damage the sensors or have a strong negative impact on measurement quality and long-term behaviour. Consequently, there is a need for new robust and accurate online-measurement systems. The purpose of this paper is to give an overview of existing online-measurement systems, to present the current state of research and to show the results of practice tests at an agricultural and industrial AD plant. It becomes obvious that a broad variety of measurement solutions have been developed over the past few years, but that the main problem is the upscaling from lab-scale to practical application at full-scale AD plants. Results from the practice tests show that an online-measurement of pH, ORP, TS is possible

Energy efficiency of wastewater treatment plants. Overview of the literature and critical discussion of energy data

3rd IWA Specialized International Conference Ecotechnologies for Wastewater Treatment 2016 (ecoSTP16). Cambridge, UK, 27-30 jun 2016In response to strong growth in energy intensive wastewater treatment, public agencies and industry began to explore and implement measures to ensure achievement of the target indicated in the 2020 Climate and Energy Package. However, in the absence of fundamental and globally recognized approach evaluating wastewater treatment plants (WWTPs) energy performance, these policies could be economically wasteful. This paper gives an overview of the literature of WWTPs energy-use performance. Energy key performance indicators (KPIs) found are presented and critically assessed, pointing out the limits to their validity. Data from more than 430 WWTPs, together with the methods for synthesizing the information is presented. 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 observe

Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement

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

Überwachung und Optimierung von Biomasse-Feuerungsanlagen mit Hilfe automatischer Bildanalyseverfahren

Die Regelung heutiger, industriell genutzter Biomasse-Feuerungsanlagen erfolgt meistens über fest eingestellte Parameter. Bei Veränderungen des Brennstoffs dienen visuelle Beobachtungen der Mitarbeiter als Basis der Neueinstellung dieser Parameter. Das Ziel der Forschung besteht in der Optimierung solcher Regelungen durch den Einsatz von Kamerasystemen in Kombination mit einer automatisierten Regelung, die auf Basis von Flammenbild-Analysen funktioniert. Ein solches System wäre auch unabhängig von der Art des Brennstoffs