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Advances of Digital Transformation Tools in Food Engineering Research: Process Simulation and Virtual Reality Applications in Production Processes

Year 2024, Volume: 1 Issue: 1, 1 - 12, 27.05.2024

Abstract

The food industry, research and education, along with the development of technologies, are faced with very broad and complex application areas. In order to meet the nutritional needs and expectations of the growing world population and to ensure food quality and food safety expectations, there is an increasing need for computer applications in the control, modeling, optimization, and data analysis of production systems. In this context, digital transformation tools have a significant impact on food engineering research and education, as well as industrial applications. The aim of this study is to examine the role of digital transformation tools in food engineering, especially process simulation and virtual reality applications in production processes. Process simulation makes it possible to analyze (technical, economic, and environmental) various scenarios by creating mathematical models of production processes in a digital environment. When process simulations communicate with real-world production systems, digital twins are created. With this application, production efficiency increases, waste is reduced, and quality is improved. Virtual and augmented reality can be used in various areas, such as training, simulation, design, and inspection in production facilities. This technology allows users to simulate real-world scenarios and understand production processes more effectively. In the next part of the study, a framework is proposed for the integration of process simulation and virtual/augmented reality applications with other digital transformation tools. It is concluded that this framework will provide a powerful structure for optimizing and improving production processes in the food industry.

References

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  • Ammann, J., Stucki, M., and Siegrist, M. (2020). True colours: Advantages and challenges of virtual reality in a sensory science experiment on the influence of colour on flavour identification. Food Quality and Preference, 86, 103998.
  • Atthajariyakul, S., and Atthajariyakul, T. (2006). Fluidized bed paddy drying in optimal conditions via adaptive fuzzy logic control. Journal of Food Engineering, 75(1), 104–114.
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  • Barzegar, M., Zare, D., and Stroshine, R. L. (2015). An integrated energy and quality approach to optimization of green peas drying in a hot air infrared-assisted vibratory bed dryer. Journal of Food Engineering, 166, 302–315.
  • Bouzembrak, Y., Klüche, M., Gavai, A., and Marvin, H. J. P. (2019). Internet of things in food safety: Literature review and a bibliometric analysis. Trends in Food Science and Technology, 94, 54-64.
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  • Koulouris, A., Misailidis, N., and Petrides, D. (2021). Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food and Bioproducts Processing, 126, 317–333.
  • Kritzinger, W., Karner, M., Traar, G., Henjes, J., and Sihn, W. (2018). Digital twin in manufacturing: a categorical literature review and classification. IFAC PapersOnLine, 51(11), 1016–1022.
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  • Mahesh, J. S., Saranya, S., Balakrishnaraja, R., and Kamble, D. B. (2024). Influence of extraction techniques on biologically enriched raw soursop fruit and comparative evaluation by response surface methodology and artificial neural network. Food and Humanity, 2, 100216.
  • Martínez-Ruano, J. A., Caballero-Galván, A. S., Restrepo-Serna, D. L., and Cardona, C. A. (2018). Techno- economic and environmental assessment of biogas production from banana peel (Musa paradisiaca) in a biorefinery concept. Environmental Science and Pollution Research, 25, 35971–35980.
  • Mel, M., Yonga, A. S. H., Avicenna, Ihsan, S. I., and Setyobudi, R. H. (2015) A simulation study for economic analysis of biogas production from agricultural biomass. Energy Procedia, 65, 204-214.
  • Munagala, M., Shastri, Y., Nalawade, K., Konde, K., and Patil, S. (2021). Life cycle and economic assessment of sugarcane bagasse valorization to lactic acid. Waste Management, 126, 52–64
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  • Rahman, M. M., Billah, A. G. M. M., Mekhilef, S., and Rahman, S. (2014). Application of genetic algorithm for optimization of solar powered drying. IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).
  • Rathnayake, M., Chaireongsirikul, T., Svangariyaskul, A., Lawtrakul, L., and Toochinda, P. (2018). Process simulation based life cycle assessment for bioethanol production from cassava, cane molasses, and rice straw. Journal of Cleaner Production, 190, 24-35.
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  • Saydam, D. D., Koulouris, A., and Dalgıç, A. C. (2020). Process simulation of integrated xanthan gum and sorbitol bioproduction: Economic and sensitivity analysis with taguchi approach. Process Integration and Optimization for Sustainability, 4, 279–295.
  • Taheri-Garavand, A., Karimi, F., Karimi, M., Lotfi, V., and Khoobbakht, G. (2018). Hybrid response surface methodology-artificial neural network optimization of drying process of banana slices in a forced convective dryer. Food Science and Technology International, 24(4), 277–291.
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Year 2024, Volume: 1 Issue: 1, 1 - 12, 27.05.2024

Abstract

References

  • Abioye, A. O., Hussein, J. B., Oke, M. O., and Bolarinwa, I. F. (2024). Modelling some quality attributes of a convective Hot-Air dried tomato slices using ANN and ANFIS techniques, Measurement. Food, 13, 100140.
  • Aghilinategh, N., Rafiee, S., Hosseinpour, S., Omid, M., and Mohtasebi, S. S. (2016). Realtime color change monitoring of apple slices using image processing during intermittent microwave convective drying. Food Science and Technology International, 22, 634–646.
  • Alba-Martínez, J., Sousa, P. M., Alcañiz, M., Cunha, L. M., Martínez-Monzó, J., and García-Segovia, P. (2022). Impact of context in visual evaluation of design pastry: Comparison of real and virtual. Food Quality and Preference, 97, 104472.
  • Ammann, J., Stucki, M., and Siegrist, M. (2020). True colours: Advantages and challenges of virtual reality in a sensory science experiment on the influence of colour on flavour identification. Food Quality and Preference, 86, 103998.
  • Atthajariyakul, S., and Atthajariyakul, T. (2006). Fluidized bed paddy drying in optimal conditions via adaptive fuzzy logic control. Journal of Food Engineering, 75(1), 104–114.
  • Barthwal, R., Kathuria, D., Joshi, S., Kaler, R. S. S, and Singh, N. (2024). New trends in the development and application of artificial intelligence in food processing. Innovative Food Science and Emerging Technologies, 92, 103600.
  • Barzegar, M., Zare, D., and Stroshine, R. L. (2015). An integrated energy and quality approach to optimization of green peas drying in a hot air infrared-assisted vibratory bed dryer. Journal of Food Engineering, 166, 302–315.
  • Bouzembrak, Y., Klüche, M., Gavai, A., and Marvin, H. J. P. (2019). Internet of things in food safety: Literature review and a bibliometric analysis. Trends in Food Science and Technology, 94, 54-64.
  • Carrasco, M. D. O and Chen, P. H. (2021). Application of mixed reality for improving architectural design comprehension effectiveness. Automation in Construction, 126, doi: https://doi.org/10.1016/j.autcon.2021.103677
  • Chen, J., Zhang, M., Xu, B., Sun, J., and Mujumdar, A. S. (2020). Artificial intelligence assisted technologies for controlling the drying of fruits and vegetables using physical fields: A review. Trends in Food Science and Technology, 105, 251-260.
  • Chen, Y., Yang, O., Sampat, C., Bhalode, P., Ramachandran, R., and Ierapetritou, M. (2020). Digital twins in pharmaceutical and biopharmaceutical manufacturing: a literature review. Processes, 8(9), 1088, http://dx.doi.org/10.3390/pr8091088.
  • Crofton, E. C., Botinestean, C., Fenelon, M., and Gallagher, E. (2019). Potential applications for virtual and augmented reality technologies in sensory science. Innovative Food Science and Emerging Technologies, 56, 102178.
  • Cruz-Domínguez, O., Carrera-Escobedo, J. L., Guzmán-Valdivia, C. H., Ortiz-Rivera, A., García-Ruiz, M., Durán-Muñoz, H. A., Vidales-Basurto, C. A., and Castaño, V. M. (2021). A novel method for dried chili pepper classification using artificial intelligence. Journal of Agriculture and Food Research, 3, 100099.
  • Dalgıç, A. C. (2018, April). Process simulation and economic evaluation of bioethanol production from industrial food wastes. International Congress on Engineering and Life Science, Kastamonu, Türkiye. ISBN: 978-605-4697-15-1
  • Dasgupta, D., Sidana, A., Ghosh, P., Sharma, T., Singh, J., Prabhune, A., More, S., Bhaskar, T., and Ghosh, D. (2021). Energy and life cycle impact assessment for xylitol production from corncob. Journal of Cleaner Production, 278, 123217.
  • Dash, K. K., Gohain, G., and Bhagyaraj, G. V. S. (2024). Hydrocolloid effect on Joha rice pancake dough rheology and sensory evaluation by fuzzy logic. Measurement: Food, 13, 100136.
  • Donaldson, A. A., Kadakia, P., Gupta, M., and Zhang, Z. (2012). Production of energy and activated carbon from agri-residue: sunflower seed example. Applied Biochemistry and Biotechnology, 168(1), 154–162.
  • Dong, Y., Sharma, C., Mehta, A., and Torrico, D. D. (2021). Application of augmented reality in the sensory evaluation of yogurts. Fermentation, 7(3), 147.
  • Dursun, D., Koulouris, A., and Dalgıç, A. C. (2020). Process simulation and techno economic analysis of astaxanthin production from agro-industrial wastes. Waste and Biomass Valorization, 11, 943– 954.
  • Erenturk, S., and Erenturk, K. (2007). Comparison of genetic algorithm and neural network approaches for the drying process of carrot. Journal of Food Engineering, 78(3), 905–912.
  • Esmaeily, R., Razavi, M. A., and Razavi, S. H. (2024). A step forward in food science, technology and industry using artificial intelligence. Trends in Food Science and Technology, 143, 104286.
  • Haenlein, M., Kaplan, A., Tan, C. W., and Zhang, P. (2019). Artificial intelligence (AI) and management analytics. Journal of Management Analytics, 6(4), 341–343.
  • Han, W., Fang, J., Liu, Z., and Tang, J. (2016). Techno-economic evaluation of a combined bioprocess for fermentative hydrogen production from food waste. Bioresource Technology, 202, 107–112.
  • Hassoun, A., Prieto, M. A., Carpena, M., Bouzembrak, Y., Marvin, H. J. P., Pallares, N., Barba, F. J., Bangar, S. P, Chaudhary, V., Ibrahim, S., and Bono, G. (2022). Exploring the role of green and Industry 4.0 technologies in achieving sustainable development goals in food sectors. Food Research International, 162, 112068.
  • Hungler, P., Thurgood, C., Marinova, M., White, S., Mirzoian, L., Thoms, M., Law, J., Chabot, M., and Moozeh, K. (2022). Design and development of an open-source virtual reality chemical processing plant. Proceedings 2022 Canadian Engineering Education Association (CEEA-ACEG22) Conference.
  • Innocenzi, V., and Prisciandaro, M. (2021). Technical feasibility of biodiesel production from virgin oil and waste cooking oil: Comparison between traditional and innovative process based on hydrodynamic cavitation. Waste Management, 122, 15–25.
  • Islam, S., and Cullen, J. M. (2021). Food traceability: A generic theoretical framework. Food Control, 123(3), 107848.
  • Jiang, H., Zhang, M., Mujumdar, A. S., and Lim, R. X. (2015). Drying uniformity analysis of pulse-spouted microwave–freeze drying of banana cubes. Drying Technology, 34, 539–546.
  • Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., and Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033.
  • Karadeniz, A. M., Arif, I., Kanak, A., and Ergün, S. (2019). Digital twin of egastronomic things: a case study for ice cream machines. 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, (pp. 1-4), doi: 10.1109/ISCAS.2019.8702679.
  • Konfo, T. R. C., Djouhou, F. M. C., Hounhouigan, M. H., Dahouenon-Ahoussi, E., Avlessi, F., and Sohounhloue, C. K. (2023). Recent advances in the use of digital technologies in agri-food processing: A short review. Applied Food Research, 3(2), 100329.
  • Koulouris, A., Misailidis, N., and Petrides, D. (2021). Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food and Bioproducts Processing, 126, 317–333.
  • Kritzinger, W., Karner, M., Traar, G., Henjes, J., and Sihn, W. (2018). Digital twin in manufacturing: a categorical literature review and classification. IFAC PapersOnLine, 51(11), 1016–1022.
  • Li, J., Li, Z., Raghavan, G. S. V., Song, F., Song, C., Liu, M., Pei, Y., Fu, W., and Ning, W. (2021). Fuzzy logic control of relative humidity in microwave drying of hawthorn. Journal of Food Engineering, 310, 110706.
  • Liberty, J. T., Sun, S., Kucha, C., Adedeji, A. A., Agidi, G., and Ngadi, M. O. (2024). Augmented reality for food quality assessment: Bridging the physical and digital worlds. Journal of Food Engineering, 367, 111893.
  • Lohrasbi, M., Pourbafrani, M., Niklasson, C., and Taherzadeh, M. J. (2010). Process design and economic analysis of a citrus waste biorefinery with biofuels and limonene as products. Bioresource Technology, 101(19), 7382–7388.
  • Mabrouki, J., Abbassi, M. A., Guedri, K., Omri, A., and Jeguirim, M. (2015). Simulation of biofuel production via fast pyrolysis of palm oil residues. Fuel, 159, 819–827.
  • Mahesh, J. S., Saranya, S., Balakrishnaraja, R., and Kamble, D. B. (2024). Influence of extraction techniques on biologically enriched raw soursop fruit and comparative evaluation by response surface methodology and artificial neural network. Food and Humanity, 2, 100216.
  • Martínez-Ruano, J. A., Caballero-Galván, A. S., Restrepo-Serna, D. L., and Cardona, C. A. (2018). Techno- economic and environmental assessment of biogas production from banana peel (Musa paradisiaca) in a biorefinery concept. Environmental Science and Pollution Research, 25, 35971–35980.
  • Mel, M., Yonga, A. S. H., Avicenna, Ihsan, S. I., and Setyobudi, R. H. (2015) A simulation study for economic analysis of biogas production from agricultural biomass. Energy Procedia, 65, 204-214.
  • Munagala, M., Shastri, Y., Nalawade, K., Konde, K., and Patil, S. (2021). Life cycle and economic assessment of sugarcane bagasse valorization to lactic acid. Waste Management, 126, 52–64
  • Nadian, M. H., Abbaspour-Fard, M. H., Sadrnia, H., Golzarian, M. R., Tabasizadeh, M., and Martynenko, A. (2016). Improvement of kiwifruit drying using computer vision system (CVS) and ALM clustering method. Drying Technology, 35(6), 709–723.
  • Nugroho, G., Tedjakusuma, F., Lo, D., Romulo, A., Pamungkas, D. H., and Kinardi, S. A. (2023). Review of the application of digital transformation in food industry. Journal of Current Science and Technology, 13(3), 774-790. https://doi.org/10.59796/jcst.V13N3.2023.1285
  • Pennanen, K., Närväinen, J., Vanhatalo, S., Raisamo, R., and Sozer, N. (2020). Effect of virtual eating environment on consumers’ evaluations of healthy and unhealthy snacks. Food Quality and Preference, 82(2), 103871.
  • Rahman, M. M., Billah, A. G. M. M., Mekhilef, S., and Rahman, S. (2014). Application of genetic algorithm for optimization of solar powered drying. IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).
  • Rathnayake, M., Chaireongsirikul, T., Svangariyaskul, A., Lawtrakul, L., and Toochinda, P. (2018). Process simulation based life cycle assessment for bioethanol production from cassava, cane molasses, and rice straw. Journal of Cleaner Production, 190, 24-35.
  • Sayar, N. A., Kazan, D., Pinar, O., Akbulut, B. S., and Sayar, A. A. (2018). Retro-techno-economic evaluation of acetic acid production using cotton stalk as feedstock. Journal of Material Cycles and Waste Management, 20, 2077–2088.
  • Saydam, D. D., Koulouris, A., and Dalgıç, A. C. (2020). Process simulation of integrated xanthan gum and sorbitol bioproduction: Economic and sensitivity analysis with taguchi approach. Process Integration and Optimization for Sustainability, 4, 279–295.
  • Taheri-Garavand, A., Karimi, F., Karimi, M., Lotfi, V., and Khoobbakht, G. (2018). Hybrid response surface methodology-artificial neural network optimization of drying process of banana slices in a forced convective dryer. Food Science and Technology International, 24(4), 277–291.
  • Tao, Z., and Chao, J. (2024). The impact of a blockchain-based food traceability system on the online purchase intention of organic agricultural products. Innovative Food Science and Emerging Technologies, 92, 103598.
  • Thapa, A., Nishad, S., Biswas, D., and Roy, S. (2023). A comprehensive review on artificial intelligence assisted technologies in food industry. Food Bioscience, 56, 103231.
  • Todorović, V., Milić, N., and Lazarević, M. (2019). Augmented reality in food production traceability-use case. IEEE EUROCON 2019-18th International Conference on Smart Technologies, (pp. 1-5), Novi Sad, Serbia.
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There are 59 citations in total.

Details

Primary Language English
Subjects Food Engineering, Basic Food Processes
Journal Section Reviews
Authors

Ali Coşkun Dalgıç 0000-0001-6806-5917

Publication Date May 27, 2024
Submission Date March 15, 2024
Acceptance Date April 22, 2024
Published in Issue Year 2024 Volume: 1 Issue: 1

Cite

APA Dalgıç, A. C. (2024). Advances of Digital Transformation Tools in Food Engineering Research: Process Simulation and Virtual Reality Applications in Production Processes. Natural Sciences and Engineering Bulletin, 1(1), 1-12.