| 1. |
- Ivan, Heidi Lynn, 1996-
(författare)
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Fault Detection in Wastewater Treatment : Process Supervision to Improve Wastewater Reuse
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As wastewater treatment plants transition to water resource recovery facilities, the need for improved control and consequently supervision increases. Despite the large volume of research that has been performed on this topic, the use in industry is scarce. Practical implementation is challenging due to the nature of the process, and a lack of standardisation in the research results in uncertainty as to the state of the art. This is one of the main challenges identified. Experimental work is performed using the Benchmark Simulation Model No. 1 to identify monitoring requirements and evaluate the performance of univariate fault detection methods. For the former, residual based process fault signatures are used to determine minimal sensor requirements based on detectability and isolability goals. Sensor faults are the focus of the latter issue, using the Shewhart, cumulative sum, and exponentially weighted moving average control charts to detect bias and drift faults in a controlled variable sensor. The use of a standard model and known fault detection methods is useful to establish a baseline for future work. Given the lack of standardised use in industry this is considered critical. Both proposed methods emphasise ease of visualisation which is beneficial for industrial implementation.
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| 2. |
- Marzi, E., et al.
(författare)
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Power-to-Gas for energy system flexibility under uncertainty in demand, production and price
- 2023
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Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 284
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Tidskriftsartikel (refereegranskat)abstract
- The growing penetration of non-programmable renewable energy sources and the consequent fluctuations in energy prices and availability lead to the need to enhance energy system flexibility and synergies between different energy vectors. This can be reached through sector integration. Among the most relevant technologies used for this purpose, Power-to-Gas systems allow excess renewable electricity to be converted directly into fuels that can be then stored or used. A smart energy system, however, which includes these innovative solutions, requires intelligent management methods to optimize its operation. This work investigates the operational strategy of energy systems integrated with Power-to-Gas solutions for seasonal storage, by developing an optimization model for the system, formulated as Mixed-Integer Linear Programming problem. The algorithm tackles the uncertain nature of future disturbances, such as energy needs, generation and price using two-stage stochastic programming. The algorithm is tested on grid-connected and 100% renewable energy supply case studies. The novel stochastic algorithm allows a more robust optimization compared to a deterministic optimization, and system management is ensured under several future disturbances realization. Furthermore, the integration of Power-to-Gas solutions warrants the energy security of the energy systems and acts as a buffer to forestall unpredictable behavior of the disturbances.
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| 3. |
- Zaccaria, Valentina, 1989-, et al.
(författare)
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BAYESIAN INFORMATION FUSION FOR GAS TURBINES DIAGNOSTICS AND PROGNOSTICS
- 2023
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Ingår i: Proc. ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791886977
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Konferensbidrag (refereegranskat)abstract
- Prognosis, or the forecasting of remaining operational life of a component, is a fundamental step for predictive maintenance of turbomachines. While diagnostics gives important information on the current conditions of the engine, it is through prognostics that a suitable maintenance interval can be determined, which is critical to minimize costs. However, mature prognostic models are still lacking in industry, which still heavily relies on human experience or generic statistical quantifications. Predicting future conditions is very challenging due to many factors that introduce significant uncertainty, including unknown future machine operations, interaction between multiple faults, and inherent errors in diagnostic and prognostic models. Given the importance to quantify this uncertainty and its impact on operational decisions, this work presents an information fusion approach for gas turbine prognostics. Condition monitoring performed by a Bayesian network is fused with a particle filter for prognosis of gas turbine degradation, and the effect of diagnostic models uncertainty on the prognosis are estimated through probabilistic analysis. Gradual and rapid degradation are simulated on a gas turbine performance model and the impact of sensor noise and initial conditions for the particle filter estimation are assessed. This work demonstrates that the combination of Bayesian networks and particle filters can give good results for short-term prognosis.
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| 4. |
- Zlatkovikj, Milan, et al.
(författare)
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Influence of fuel properties on the performance of the feed forward model predictive control (FF MPC) for biomass boilers
- 2023
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Ingår i: Energy Proceedings. - : Scanditale AB. - 2004-2965. ; 32, s. 1-6
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Tidskriftsartikel (refereegranskat)abstract
- The growing share of renewable energy sources drives the need for increased flexibility in the energy systems. The flexibility provision from thermal plants is limited by the boiler’s thermal inertia as a bottleneck. Advanced controllers, such as model predictive control (MPC), have been identified as potential flexibility enablers. Fuel properties are crucial input for controllers. This work investigated the feasibility of using the properties obtained online by using near infrared spectroscopy based soft sensor to further improve the control performance. The performance of the existing proportional integral (PI) controller is compared with those of 2 feed forward (FF) MPC controllers. Both FF MPCs have significant improvement compared to PI controller and the FF MPC based on the full elemental composition shows the best performance due to more complete fuel information. There is a potential for revenues improvement with advanced control up to 1050 euros for one operation day.
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| 5. |
- Zlatkovikj, Milan, et al.
(författare)
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Influence of the transient operation of a large-scale thermal energy storage system on the flexibility provided by CHP plants
- 2023
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Ingår i: e-Prime - Advances in Electrical Engineering, Electronics and Energy. - : Elsevier Ltd. - 2772-6711. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Among many technical options to improve the flexibility in combined heat and power (CHP) plants, thermal energy storage (TES) has attracted the most attention with its high applicability and benefit. Previous studies normally adopted a simplified approach for modelling a TES system, which assumes the optimized charged or discharged rate of heat can always be realized within the rated capacity. However, this may yield unfeasible results as the charging and discharging rates are dependent on the dynamic status of a TES, such as the state of charge (SOC) and water temperature, and the water flowrate for charging and discharging. In order to consider the transient operation of a TES, a 1D dynamic model was developed and validated against measured data from a real CHP plant. To analyze the dynamic performance of a TES, two key performance indicators (KPI), the maximum charging/discharging rate (C/D-ratemax) and the constant maximum charging/discharging rate that can be maintained constantly for one hour (CC/CD-ratemax) were employed. By doing simulations, it has been found that the CC/CD-ratemax was lower than the C/D-ratemax for most given SOCs of the studied TES. The developed model was also used to examine the optimized operation of a TES for providing flexibility. Some unfeasible results have been identified, as the optimized hourly charging/discharging rates were constrained by the CC/CD-ratemax. Therefore, it is of great importance to integrate a detailed dynamic model when optimizing the dispatch of electricity and heat for a CHP plant.
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