Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products

Process simulation and evaluation of scaled-up biocatalytic systems: Advances, challenges, and future prospects

With the increased demand for bio-based products and the rapid development of biomanufacturing technologies, biocatalytic reactions including microorganisms and enzyme based, have become promising approaches. Prior to the scale-up of production process, environmental and economic feasibility analysis are essential for the development of a sustainable and intelligent bioeconomy in the context of industry 4.0. To achieve these goals, process simulation supports system optimization, improves energy and resource utilization efficiencies, and supports digital bioprocessing. However, due to the insufficient understanding of cellular metabolism and interaction mechanisms, there is still a lack of rational and transparent simulation tools to efficiently simulate, control, and optimize microbial/enzymatic reaction processes. Therefore, there is an urgent need to develop frameworks that integrate kinetic modeling, process simulation, and sustainability analysis for bioreaction simulations and their optimization. This review summarizes and compares the advantages and disadvantages of different process simulation software and models in simulating biocatalytic processes, identifies the limitations of traditional reaction kinetics models, and proposes the requirement of simulations close to real reactions. In addition, we explore the current state of kinetic modeling at the microscopic scale and how process simulation can be linked to kinetic models of cellular metabolism and computational fluid dynamics modeling. Finally, this review discusses the requirement of sensitivity analysis and how machine learning can assist with optimization of simulations to improve energy efficiency and product yields from techno-economic and life cycle assessment perspectives.

Recombinant proteins production in Escherichia coli BL21 for vaccine applications: a cost estimation of potential industrial-scale production scenarios

Recent SARS-CoV-2 pandemic elevated research interest in microorganism-related diseases, and protective health application importance such as vaccination and immune promoter agents emerged. Among the production methods for proteins, recombinant technology is an efficient alternative and frequently preferred method. However, since the production and purification processes vary due to the protein nature, the effect of these differences on the cost remains ambiguous. In this study, brucellosis and its two important vaccine candidate proteins (rOmp25 and rEipB) with different properties were selected as models, and industrial-scale production processes were compared with the SuperPro Designer® for estimating the unit production cost. Simulation study showed raw material cost by roughly 60% was one of the barriers to lower-cost production and 52.5 and 559.8 $/g were estimated for rEipB and rOmp25, respectively.

Virtualization and digital twins of the food supply chain for enhanced food safety

Meeting food safety requirements without jeopardizing quality attributes or sustainability involves adopting a holistic perspective of food products, their manufacturing processes and their storage and distribution practices. The virtualization of the food supply chain offers opportunities to evaluate, simulate, and predict challenges and mishaps potentially contributing to present and future food safety risks. Food systems virtualization poses several requirements: (1) a comprehensive framework composed of instrumental, digital, and computational methods to evaluate internal and external factors that impact food safety; (2) nondestructive and real-time sensing methods, such as spectroscopic-based techniques, to facilitate mapping and tracking food safety and quality indicators; (3) a dynamic platform supported by the Internet of Things (IoT) interconnectivity to integrate information, perform online data analysis and exchange information on product history, outbreaks, exposure to risky situations, etc.; and (4) comprehensive and complementary mathematical modeling techniques (including but not limited to chemical reactions and microbial inactivation and growth kinetics) based on extensive data sets to make realistic simulations and predictions possible. Despite current limitations in data integration and technical skills for virtualization to reach its full potential, its increasing adoption as an interactive and dynamic tool for food systems evaluation can improve resource utilization and rational design of products, processes and logistics for enhanced food safety. Virtualization offers affordable and reliable options to assist stakeholders in decision-making and personnel training. This chapter focuses on definitions and requirements for developing and applying virtual food systems, including digital twins, and their role and future trends in enhancing food safety.

Unleashing the Potential of Digitalization in the Agri-Food Chain for Integrated Food Systems

Digitalization transforms many industries, especially manufacturing, with new concepts such as Industry 4.0 and the Industrial Internet of Things. However, information technology also has the potential to integrate and connect the various steps in the supply chain. For the food industry, the situation is ambivalent: It has a high level of automatization, but the potential of digitalization is so far not used today. In this review, we discuss current trends in information technology that have the potential to transform the food industry into an integrated food system. We show how this digital transformation can integrate various activities within the agri-food chain and support the idea of integrated food systems. Based on a future-use case, we derive the potential of digitalization to tackle future challenges in the food industry and present a research agenda.

Advancements and challenges of digital twins in industry

Digital twins, which are considered an effective approach to realize the fusion between virtual and physical spaces, have attracted a substantial amount of attention in the past decade. With their rapid development in recent years, digital twins have been applied in various fields, particularly in industry. However, there are still some gaps to be filled and some limitations to be addressed. Here we provide a brief overview of digital twin advancements in industry and highlight the main pitfalls to avoid and challenges to overcome, to improve the maturity of digital twins and facilitate large-scale industrial applications in the future.

Enhancing shipyard transportation efficiency through dynamic scheduling using digital twin technology

Uncertainties, such as road restrictions at shipyards and the irregular shape of blocks, pose challenges for transporter scheduling. Efficient scheduling of multiple transporters is critical to improving transportation efficiency. The digital twin (DT) technology offers numerous benefits, enabling interactions between the virtual and real worlds, real-time mapping, and dynamic performance evaluation. Based on DT technology, this study proposes a dynamic scheduling approach for cooperative transportation utilizing multiple transporters. The scheduling problem for multiple transporters is addressed and modeled in this study, considering factors such as block size and transporter loading. To solve this problem, a framework of DT-based multiple transporters system is established in a virtual environment. By inputting block information into this system, a solution is generated using transporter scheduling rules and interference detection methods. Experimental comparisons are conducted in this paper, exploring various scenarios with different number of tasks and the application of DT. The results demonstrate that the proposed approach effectively enhances transportation efficiency and improves ship construction efficiency. Hence, this study expands the application of DT technology in dynamic scheduling of transportation in shipyards and provides new ideas for shipbuilding company managers.

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

Computer-aided food engineering

Computer-aided food engineering (CAFE) can reduce resource use in product, process and equipment development, improve time-to-market performance, and drive high-level innovation in food safety and quality. Yet, CAFE is challenged by the complexity and variability of food composition and structure, by the transformations food undergoes during processing and the limited availability of comprehensive mechanistic frameworks describing those transformations. Here we introduce frameworks to model food processes and predict physiochemical properties that will accelerate CAFE. We review how investments in open access, such as code sharing, and capacity-building through specialized courses could facilitate the use of CAFE in the transformation already underway in digital food systems.

Digital Food Twins Combining Data Science and Food Science: System Model, Applications, and Challenges

The production of food is highly complex due to the various chemo-physical and biological processes that must be controlled for transforming ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of the raw materials, such a digital twin has to take into account not only the processing steps but also the chemical, physical, or microbiological properties that change the food independently from the processing. We propose a hybrid modeling approach, which integrates the traditional approach of food process modeling and simulation of the bio-chemical and physical properties with a data-driven approach based on the application of machine learning. This work presents a conceptual framework for our digital twin concept based on explainable artificial intelligence and wearable technology. We discuss the potential in four case studies and derive open research challenges.

Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

Computer-Aided Modeling, Simulation, and Exergy Analysis of Large-Scale Production of Magnetite (Fe3O4) Nanoparticles via Coprecipitation

Magnetite nanoparticles present attractive properties including high magnetization, low toxicity, adsorption capacity, and simple preparation, making them efficient in water purification processes, soil remediation, and biomedical applications. In this sense, there is growing interest in the production of magnetite nanoparticles; therefore, evaluating the performance of this process on a large scale gives relevant information to process designers. In this work, the simulation and exergy analysis of large-scale production of magnetite nanoparticles via coprecipitation were performed using computer-aided tools. The process was modeled for the production of 807 t/year of magnetite nanoparticles; the data for the simulation were obtained from the literature, and experimental results were developed by the authors. The exergy efficiency of the process was estimated at 0.046%. The exergy of waste was estimated to be 105 313 MJ/h, while the unavoidable exergy losses were 2941 MJ/h. Washing 2 and 3 represented the most critical stages of the process, contributing 95.12% of the total irreversibilities due to the waste exergy, which corresponds to the water and ethanol exergy discarded in these stages. These results show that the process must be improved from the energy point of view and require the implementation of process optimization strategies to reach a more sustainable design.