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Digital twins have gained a lot of attention in modern day industry, but practical challenges arise from the requirement of continuous and real-time data integration. The actual physical systems are also exposed to disturbances unknown to the real-time simulation. Therefore, adaptation is required to ensure reliable performance and to improve the usability of digital twins in monitoring and diagnostics. This study proposes a general approach to the real-time adaptation of digital twins based on a mechanism guided by evolutionary optimization. The mechanism evaluates the deviation between the measured state of the real system and the estimated state provided by the model under adaptation. The deviation is minimized by adapting the model input based on the differential evolution algorithm. To test the mechanism, the measured data were generated via simulations based on a physical model of the real system. The estimated data were generated by a surrogate model, namely a simplified version of the physical model. A case study is presented where the adaptation mechanism is applied on the digital twin of a marine thruster. Satisfactory accuracy was achieved in the optimization during continuous adaptation. However, further research is required on the algorithms and hardware to reach the real-time computation requirement.

Biomethane is a renewable product that can directly substitute its fossil counterpart, although its synthesis from residual biomasses has some hurdles. Because of the complex nature of both biomasses and the microbial consortia involved, innovative approaches such as mathematical modeling can be deployed to support possible improvements. The goal of this study is two-fold, as we aimed to modify a part of the Anaerobic Digestion Model No. 1 (ADM1), describing biomethane production from activated sludge, matching with its actual microbial nature, and to use the model for identifying relevant parameters to improve biomethane production. Firstly, thermodynamic analysis was performed, highlighting the direct route from glucose to biomethane as the most favorable. Then, by using MATLAB® and Simulink Toolbox, we discovered that the model fails to predict the microbiological behavior of the system. The structure of the ADM1 model was then modified by adding substrate consumption yields in equations describing microbial growth, to better reflect the consortium behavior. The updated model was tested by modifying several parameters: the coefficient of decomposition was identified to increase biomethane production. Approaching mathematical models from a microbiological point of view can lead to further improvement of the models themselves. Furthermore, this work represents additional evidence of the importance of informatics tools, such as bioprocess simulations to foster biomethane role in bioeconomy.

The energy industry is striving for cost-efficient production with low emissions. The efficiency of energy conversion processes is one of the most important factors affecting the path to such a goal. This paper provides a framework for the monitoring of steam boiler operation in power stations. The framework is based on the use of historical process data as a reference for real-time operation. The actual boiler efficiency is monitored together with its expected efficiency, which is an estimate of the highest historical efficiency in the corresponding process state, based on a data-driven model. In the presented approach, the process state is defined on the basis of variables that have the strongest correlation with boiler efficiency according to information-theoretic variable ranking. Boiler performance is monitored using a statistical process control chart for the difference between the expected and actual efficiencies. The framework was tested using data from a circulating fluidised bed boiler and from a corner-fired boiler. The results revealed that the strongest correlations between process variables and boiler efficiency are substantially consistent in both cases. Moreover, the framework provides a novel measure for boiler performance enhancement.

An advanced model-based control method for the integrated fuel processing and a fuel cell system consisting of ethanol reforming, hydrogen purification, and a proton exchange membrane fuel cell is presented. For process identification, a physical model of the process chain was constructed. Subsequently, the simulated process was approximated with data-driven control models. Based on these control models, a hierarchical control framework consisting of model predictive controller and a global optimization algorithm was introduced. The performance of the new control method was evaluated with simulations. Results indicate that the new optimization concept enables resource efficient and fast control of the studied energy conversion process. Fast and efficient fuel cell process could then provide sustainable power source for autonomous and mobile applications in the future.

As a part of the H2020 SPIRE project, Intensified by Design (IbD®), mineral beneficiation process chain will be intensified in terms of more efficient usage of energy and raw materials. The work focuses on optimizing control of the grinding circuit, coarse flotation process, new solids measurements and soft-sensors, as well as adaptive dynamic mass balancing. All demonstrators are interconnected through plant-wide optimization. In this paper, the process intensification activities carried out at the mini-pilot scale mineral beneficiation plant are briefly described. The real-time adaptation strategy utilizing Differential Evolution for parameter estimation in a sliding window is presented. The initial results show a successful parameter estimation result even in a short time window.

A nonlinear empirical model between stress and Barkhausen noise is identified in this study. The identification procedure uses a genetic algorithm followed by a Nelder-Mead optimization procedure. The model is identified with the data set where an external load is applied to RAEX400 low alloyed hot-rolled steel samples. The results of the study show that the identified model performs well in stress predictions. The identified model includes three terms which are in accordance with the literature.

A model-based control strategy for the compensation of combustion power fluctuations in small-scale biomass fired boilers is presented. Delay-free heat output model is obtained by utilising combustion temperature measurements. Local principal component regression models are constructed to cope with changing combustion conditions and redundant input variables found by data analysis. The modelling approach was validated using data from 30 kW and 300 kW boilers. Usability of the discussed monitoring method for compensating fuel power disturbances by the primary air control was tested in practice. The results imply that modelling information can be utilised as a part of the control structure, enabling the cost-effective way to reduce emissions and maintain optimal combustion conditions in the distributed energy production with solid biomass fuels.

A procedure for surveying process data sets is presented. For this, linear models constructed in varying length, sliding data windows to determine the usefulness of data segments for process identification are utilised. The discussed approach has been applied to an industrial wood debarking plant and a biomass boiler analysis, enabling the preliminary study of process variables and conditions affecting the non-optimal process conditions. In addition, main process interactions and delays were easily discovered from the structures of the interpretable linear model candidates. It is concluded that the analysis can provide valuable information also for modelling and control of continuous processes.

A soft computing monitoring approach for automated screw insertions is presented. A model based monitoring method is developed with systematically collected experimental data and fundamental process knowledge to verify the quality of assemblies. The model for quality monitoring is based on Linguistic Equations (LE)-a non-linear scaling framework for model variables. Fuzzy reasoning and basic statistical methods are combined to interpret the model residuals and faults. Preliminary tests indicate that the proposed method could successfully cope with changes in manufacturing parameters. Based on the results, the method seems to provide valuable information for quality control of the screw insertion task.

Electromagnetic interferences and other disturbances in industrial environment may decrease the performance of wireless sensor networks. However, the lack of empirical results providing proof for these arguments is evident. In this paper, analysis results of systematic experiments carried out for potential, disturbing factors in industrial environment are presented. Moreover, a novel entropy-based approach to measure changes in jitter variation is introduced. The selected approach performed well, being able to point out statistically significant sources of disturbances.

Statistical process control (SPC) is a well-known method to perform quality control. However, this method is not adapted to small batches or prototype production because it usually doesn't provide feedback within a proper timeline. This paper describes a case study in which statistical based methods derived from classical SPC have been modified and implemented as part of a decision support system for a semiconductor process. Quality indexes have been defined to monitor the production, perform fault diagnosis and provide control information to operators. The system has been implemented in an electronics factory producing printed circuit board assemblies (PCBA) for telecommunication systems. With products in their prototype stage, over 40% of the production was defected requiring rework, only few data from non-consecutive measurements was available. Implementing a decision support system in such an environment was therefore a challenging task. Nevertheless, the method described in this paper provided adequate information to adjust the process when needed or to tune the system in case of false alarms. Moreover, line worker being supposed to have an active role in terms of quality; it was important to involve them in the design of the system. A greater interest from their side also improved their own understanding of the process and its problems.