A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems

The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

Automated Insulin Delivery (AID) are systems developed for daily use by people with type 1 diabetes (T1D). To ensure the safety of users, it is essential to consider how the human factor affects the performance and safety of these devices. While there are numerous publications on hardware-related failures of AID systems, there are few studies on the human component of the system. From a control point of view, people with T1D using AID systems are at the same time the plant to be controlled and the plant operator. Therefore, users may induce faults in the controller, sensors, actuators, and the plant itself. Strategies to cope with the human interaction in AID systems are needed for further development of the technology. In this paper, we present an analysis of potential faults introduced by AID users when the system is under normal operation. This is followed by a review of current fault tolerant control (FTC) approaches to identify missing areas of research. The paper concludes with a discussion on future directions for the new generation of FTC AID systems.

Advanced Methods for Kiln-Shell Monitoring to Optimize the Waelz Process for Zinc Recycling

The recycling of zinc in the Waelz process is an important part of the efficient use of resources in the steel processing cycle. The pyro-metallurgical processing of zinc-containing wastes takes place in a Waelz rotary kiln. Various measured variables are available to monitor the process. The temperature of the kiln-shell is analyzed by an infrared kiln-shell-scanner. In this paper, methods are presented which eliminate external weather-related disturbances on the temperature measured by the kiln-shell-scanner using a weather model and which extend the monitoring of the regularly necessary melting process to remove accretions. For this purpose, an adapted sigmoid estimation is introduced for the melting process, which provides new information about the current process status and a forecast of the further development of the melting process. As an assistance system for the plant operator, this enables an efficient execution of the melting process and reduces downtimes.

Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

In recent years, the flexibility of energy systems has become essential due to the growing penetration of renewable energy sources. The producers and consumers can enhance this flexibility by enabling a given amount of power that they can produce or consume in every condition. This is made available to the grid operator to globally optimize the dispatch management and to stabilize the grid. However, this can interfere with the operation of production units such as cogeneration plants, which also have to meet thermal demand. Therefore, producers and consumers require smart controllers to comply with grid operator requests at any time. This paper proposes a robust control strategy based on Model Predictive Control, which manages distribution networks and production plants by considering the uncertainty of the requirements for flexibility from the grid operator. The simulation case study is the district heating network of a school complex supplied by a Combined Heat and Power plant and a Thermal Energy Storage tank. The robustness of the proposed optimization is investigated by simulating several scenarios with different degrees of uncertainty about the request for electricity from the grid operator. The results show that the plant operator is able to comply with the electricity requirements to different extents depending on the degree of uncertainty and on system design choices. These considerations make it possible to improve the plant design and production planning from the perspective of grid flexibility.

More Sustainable Bioenergy by Making Use of Regional Alternative Biomass?

Bioenergy is a building block of the ongoing transformation toward renewables-based energy systems. Bioenergy supply chains are regionally embedded and need to be seen in a place-based context with specific characteristics and constraints. Using a German case study, the potential of regionally embedded bioenergy chains in the past and the future is analyzed and discussed in this paper. The analysis integrates socio-ecological data and applies sustainability criteria in a multi-criteria decision analysis (MCDA) using the Preference Ranking Organization Method for Enriched Evaluation (PROMETHEE) methodology. The case study is focused on an industrial biogas fermenter in northwestern Germany, which currently uses predominantly maize as a substrate for bioenergy. Objectives for future development according to the ambitions of the UN Sustainable Development Goals and the EU Renewable Energy Directive (RED II) discussion are set and include the involvement of the farmer as biogas plant operator and other regional stakeholders. Since the focus of the research is put on the contribution of alternative biomass, such as grass, for the optimization of bioenergy settings, the question concentrates on how different mixtures of alternative biomass can be embedded into a sustainable management of both the landscape and the energy system. The main findings are threefold: (i) bioenergy supply chains that involve alternative biomass and grass from grasslands provide optimization potentials compared to the current corn-based practice, (ii) with respect to more sustainable practices, grass from grassland and alternative bioenergy supply chains are ranked higher than chains with increased shares of corn silage, and, more generic, (iii) optimization potentials relate to several spheres of the social–ecological system where the bioenergy structure is embedded. To conclude, sustainable enablers are discussed to realize optimization potentials and emphasize the integration of regional stakeholders in making use of alternative biomass and in making regional bioenergy more sustainable.

The Factorial Analysis of the Activated Sludge Treatment Process Parameters

The main environmental problems comprise two main directions: air and water. Water treatment plant is a critical infrastructure, especially in large cities. The activated sludge process is a typical example of highly nonlinear system. The associated models found in literature are mainly analytical and complex. In this paper there are proposed data-driven models obtained from plant operation data. A factor reduction procedure, namely principal component analysis is used to find meaningful correlations between process measurements. The selected correlations are obtained via simple and multiple regression algorithm. The resulted models are specific to the studied plant, simpler than the analytical ones, and with sufficient accuracy if used in plant monitoring and operation. The proposed procedure of using data-driven models for inferential measuring decreases the analysis costs (even eliminating the necessity of measuring equipment). If the experience in operating the plant is used to predict parameter trends this procedure can provide a useful tool for developing a decision support system for the plant operator. A real time prediction module associated with a warning system can be applied for every active sludge process, having as condition the availability of plant operating data.

Anaerobic Digestate Treatment Selection Model for Biogas Plant Costs and Emissions Reduction

Agriculture is one of the leading sectors on the global level contributing to greenhouse gas (GHG) emissions increase. With the utilization of biogas production technology within the agriculture sector, ecological benefits could be achieved, with immediate economic profit. Therefore, to retain economic profit and environmental sustainability, implementation of bioeconomy principles is of key importance. This paper examines four options of digestate treatment, which is identified as one of the highest operational cost for the biogas plant. A simple and robust model in Excel Solver was developed to determine the best solution for minimising GHG emissions and maximise profit for the biogas plant operator, through an upgrade of the plant with digestate treatment technologies. The model was implemented on a case of a Croatian biogas plant and the best solution in terms of GHG reduction and profit increase proved to be fertilizer production (Option 1), through a crystallization process of struvite within the digestate. This option obtains a significant reduction in GHG emissions compared to standard biogas production without additional upgrades (Option 4), by over 90%, and increase of profit for the biogas plant operator, which diversifies the income source and creates multiple positive impacts on the environment.

Occurrence of Thermophilic Microorganisms in Different Full Scale Biogas Plants

Background: In recent years, various substrates have been tested to increase the sustainable production of biomethane. The effect of these substrates on methanogenesis has been investigated mainly in small volume fermenters and were, for the most part, focused on studying the diversity of mesophilic microorganisms. However, studies of thermophilic communities in large scale operating mesophilic biogas plants do not yet exist. Methods: Microbiological, biochemical, biophysical methods, and statistical analysis were used to track thermophilic communities in mesophilic anaerobic digesters. Results: The diversity of the main thermophile genera in eight biogas plants located in the Czech Republic using different input substrates was investigated. In total, 19 thermophilic genera were detected after 16S rRNA gene sequencing. The highest percentage (40.8%) of thermophiles was found in the Modřice biogas plant where the input substrate was primary sludge and biological sludge (50/50, w/w %). The smallest percentage (1.87%) of thermophiles was found in the Čejč biogas plant with the input substrate being maize silage and liquid pig manure (80/20, w/w %). Conclusions: The composition of the anaerobic consortia in anaerobic digesters is an important factor for the biogas plant operator. The present study can help characterizing the impact of input feeds on the composition of microbial communities in these plants.