Digital twin virtual entities for online improvements in food and renewable energy operations

Abstract

The digital twin (DT) is one of Industry 4's enabling technologies, aiming to improve the performance of physical entities through the use of a virtual counterpart. DT is a real-time representation of a system or physical entity that can adapt to operational changes in real time by using data and information gathered online to predict the physical twin's future. Despite all the advancements in both the industrial and academic sectors, there are still challenges for which DT adaptation requires more investigation. DT has been developed and used for three major purposes: service, design, and manufacturing for general industrial purposes. To develop DT to achieve all three major purposes is not realistic since different industries have different implementation orders of DT depending on their hardware/software level, and maturities of service, design and manufacturing. In this project, we will focus on DT virtual entity development purposes based on their own situations to implement. Therefore, this research will focus on developing virtual entities for the identified gaps in industry application. Full implementation of ‘proper’ DT requires twinning between the virtual and physical entities. However, this is complicated by computational effort, limitation of online measurements, and process complexity. This work introduces new approaches for processes in which mechanistical methods alone are incapable of providing DT virtual entities. The processes addressed are cream cheese fermentation and PCM energy storage, in the dairy and renewable energy industries respectively. These processes pose distinct challenges, necessitating novel approaches that combine data-driven and mechanistical methods. Process design has advanced significantly in dairy industry as a result of the high global demand for dairy products; however, process operations (manufacturing) require further improvements in order to increase product throughput while maintaining consistent quality. DT can become an important tool toward improving the online process operation in the dairy. However, proper virtual representations of systems using DT introduces a significant challenge in the dairy industry due to the complexity of mechanisms, raw material variations, and the limitations of online measurements. These factors reduce the fidelity of current industry and academic models. Additionally, developing DT for new renewable energy technologies is one of the most critical gaps. Online solutions can help improve the economic viability of such technologies, by improving energy management during operation. The DT application for improving online operations necessitates a real time solution. However, this is challenging due to the complexity and nonlinearity of the systems involved in renewable energy technologies. Two DT virtual entity development directions for the aforementioned industries are investigated in this study. The results can aim in energy savings in New Zealand which is necessary for achieving Zero Carbon Act objectives. The first is in the dairy industry, which is vital to New Zealand's economy and consumes a significant amount of energy. A cream cheese production unit including fermentations vats is studied. DT virtual entity needs to optimize the energy while meeting the quality standards by providing online scheduling of the vats. The second process involves using energy storage and solar air collectors as renewable energy sources. This system is well suited to New Zealand's temperate climate. However, this system's economic viability must be improved while meeting energy demands. DT can optimise the economical design of solar collectors and energy storages. DT can also provide an optimal control solution, allowing the system to save even more energy and cost. In the cream cheese fermentation unit, the batch duration variation is the main challenge of optimizing the vats scheduling since mechanistic modelling of the dynamics of the fermentation process is difficult and includes many time-varying parameters. The other case introduced a different challenge, the mechanistic model of the system is available for active phase change material (PCM) systems; however, its complexity makes the optimization using that model computationally infeasible. To provide solutions, combination of data-driven and mathematical modelling as well as optimization were applied. The detailed virtual entity developments for the two industries are introduced in the following paragraphs. The first process investigated the scheduling of cream cheese fermentation vats. The scheduling of fermentation vats was complicated by batch duration variations and the limitations of online measurements. For the first time in this work, a DT virtual entity was developed to schedule the cream cheese fermentation vats. The scheduling framework was twined with the fermentation vats for filling and draining them using the information for predicting the batch durations required for reaching the quality. The pH predictive models were studied because pH is an important quality indicator. Novel grey and black box models were developed for pH prediction. It was possible to create grey and black box models that could use available online measurements to improve pH prediction. Only one fermentation had a large error of 34.09 %, and the average network percentage relative error was less than 14 %. However, the results showed that the pH prediction at the final point matches the experimental data in all the test fermentations. The pH predictive models were applied in a scheduling framework. The scheduling challenge is due to the inability to predict batch durations from the fermentation start time. This causes batches interferences during draining, resulting in higher energy consumption and waste. The new DT virtual entity for scheduling presented a mathematical programming optimization that was formulated to solve this problem by assuming an initial default batch duration at the start time and updating the batch duration later based on the pH prediction model later when enough measurements were available. The 12 hours initial batch duration led to the best results in reducing idle time, energy consumption, and waste. In the second process, an energy storage was coupled with a solar air collector for supplying heat to a hut in Auckland. The model of the entire system plays an important role for achieving DT virtual entity. The entire system's first principles models were developed and validated using experimental data for this purpose. The results confirmed the dynamics model's reliability, with an average mean square error of 4°C between measured and predicted hut temperatures over 11 days. The validated model was used for designing. For the first time in this research, economical designing of the system was carried out for various application scenarios in Auckland. The results showed that the optimum surface area of the solar collector was the same at 1 square meter for all the scenarios; however, the optimum amount of PCM mass for service, domestic, and office scenarios was 35 kg, 20 kg, and zero, respectively. The model was also used for developing a virtual entity for optimal control of the system. The desired optimal control should provide online solutions despite the system complexity coming from the nonlinearity and existence of binary variables. A novel optimal control was developed using reinforcement learning (RL). For addressing the approach advantages, it was compared with a model predictive control (MPC), as a classical approach. RL as a data driven optimal control can be trained offline and provide real time solutions. A novel formulation was proposed that allowed for the adjustment of a reward to prioritise between thermal comfort and energy cost savings. By prioritizing the cost saving, 97% more cost was saved compared to prioritizing thermal comfort case, however, the offset from the desired temperature was 54% less. A balance between energy cost savings and thermal comfort was achieved by adjusting the reward.