ADORe-SNG - Comprehensive Automation, Digitalisation & Optimization of Renewable & Sustainable SNG-production
Project Description
Global warming calls for urgent measures to reduce greenhouse gas emissions. A promising alternative to fossil fuels is synthetic natural gas (SNG), which can be produced from biomass. This research project aims to maximize the efficiency and cost-effectiveness of SNG production through innovative technological approaches.
The key elements of the SNG production process are shown in the figure below. These include dual fluidized bed (DFB) gas production, gas cleaning, fluidized bed methanation and SNG upgrading. These technologies have already been successfully implemented on various scales, from pilot plants to industrial demonstration projects.
Research activities in recent decades have led to the construction of numerous large-scale biomass gasification plants for the production of electricity and heat from biomass in the form of wood chips. At the same time, the production of SNG from woody biomass has also been successfully demonstrated in Güssing and Gothenburg.
Despite the technological advances in these areas, comprehensive automation, digitalization and holistic process optimization of SNG production have been largely neglected to date. This offers enormous potential for increasing efficiency and reducing costs, which needs to be tapped into.
Such plants are currently operated either manually or with single-input single-output controllers that control specific process variables. Manual control requires considerable effort for the plant operator to keep the process constant despite variable disturbances such as fluctuating fuel quality. In addition, the efficiency of operation varies depending on the experience of the plant operator. Automating SNG production can relieve the burden on plant operators and ensure consistent operation close to the optimum operating point.
Focus of the Project
This research project aims to systematically optimize, automate and digitalize the entire process chain of SNG production from biomass. The most important focal points of the project are
- Process optimization: the aim is to increase the efficiency of SNG production. This is achieved through the use of advanced simulation methods that enable a detailed analysis and optimization of the entire process chain.
- Automation and control: The SNG production process is largely automated through the development and implementation of advanced modeling and control strategies. This includes the modeling of physical processes, the identification of relevant parameters and the application of model predictive control (MPC) as well as traditional PI control.
- Digitalization: The integration of digital technologies is intended to improve the monitoring, control and optimization of the entire process. This includes cloud-based data management and transmission solutions as well as the development of visualizations and web dashboards for real-time monitoring of process data.
- Investigation of scalability and techno-economics: A techno-economic analysis is carried out on the basis of process simulations to investigate the economic viability of both the TU Wien pilot plant and industrial-scale plants.
As part of the project, tests were carried out on the 100 kW pilot plant at TU Wien. The process chain up to fluidized bed methanation was implemented at this plant. The SNG processing was investigated by simulation. Furthermore, the transferability of the concepts to larger plants was investigated, for which measurement data from the 1 MW demonstration plant operated by BEST was available.
Publications
Bartik, Alexander. "SNG from biogenic residues., opens an external URL in a new window" PhD diss., Technische Universität Wien, 2024.
Stanger, Lukas, Alexander Bartik, Martin Hammerschmid, Stefan Jankovic, Florian Benedikt, Stefan Müller, Alexander Schirrer, Stefan Jakubek, and Martin Kozek. "Model predictive control of a dual fluidized bed gasification plant., opens an external URL in a new window" Applied Energy 361 (2024): 122917.
Vogler, Jonas, Lukas Stanger, Alexander Bartik, Alexander Schirrer, and Martin Kozek. "Soft Sensor Design for Product Gas Composition Monitoring Including Fault Isolation in a Dual Fluidized Bed Biomass Gasifier., opens an external URL in a new window" In 2024 International Conference on Control, Automation and Diagnosis (ICCAD), pp. 1-6. IEEE, 2024.
Stanger, Lukas, Alexander Bartik, Alexander Schirrer, Stefan Jakubek, and Martin Kozek. "Predictor-Based Gas Flow Rate Control With Event-Triggered Corrections., opens an external URL in a new window" In 2024 32nd Mediterranean Conference on Control and Automation (MED), pp. 525-530. IEEE, 2024.
Stanger, Lukas, Alexander Schirrer, Florian Benedikt, Alexander Bartik, Stefan Jankovic, Stefan Müller, and Martin Kozek. "Dynamic modeling of dual fluidized bed steam gasification for control design., opens an external URL in a new window" Energy 265 (2023): 126378.
Hammerschmid, Martin, Daniel Cenk Rosenfeld, Alexander Bartik, Florian Benedikt, Josef Fuchs, and Stefan Müller. "Methodology for the Development of Virtual Representations within the Process Development Framework of Energy Plants: From Digital Model to Digital Predictive Twin—A Review., opens an external URL in a new window" Energies 16, no. 6 (2023): 2641.
Stanger, Lukas, Alexander Schirrer, Alexander Bartik, and Martin Kozek. "Minimum-Variance Model Predictive Control for Dual Fluidized Bed Circulation Control., opens an external URL in a new window" IFAC-PapersOnLine 56, no. 2 (2023): 2701-2706.
Cooperation Partners
Duration
- March 2021 - April 2024
