Vector-Tensor multiplet in N=2 superspace with central charge

We use the four-dimensional N=2 central charge superspace to give a geometrical construction of the Abelian vector-tensor multiplet consisting, under N=1 supersymmetry, of one vector and one linear multiplet. We derive the component field supersymmetry and central charge transformations, and show that there is a super-Lagrangian, the higher components of which are all total derivatives, allowing us to construct superfield and component actions.Comment: LaTeX2e with AMS-LaTeX, 12 page

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

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

Multi-objective nonlinear model predictive substrate feed control of a biogas plant

In this paper a closed-loop substrate feed control for agricultural biogas plants is proposed. In this case, multi-objective nonlinear model predictive control is used to control composition and amount of substrate feed to optimise the economic feasibility of a biogas plant whilst assuring process stability. The control algorithm relies on a detailed biogas plant simulation model using the Anaerobic Digestion Model No. 1. The optimal control problem is solved using the state-of-the-art multi-objective optimization method SMS-EGO. Control performance is evaluated by means of a set point tracking problem in a noisy environment. Results show, that the proposed control scheme is able to keep the produced electrical energy close to a set point with an RMSE of 0.9 %, thus maintaining optimal biogas plant operation

Multi-fidelity Modeling and Optimization of Biogas Plants

An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, Multi-fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension

Deep Learning in Resource and Data Constrained Edge Computing Systems

To demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C-MAPSS dataset