Data Reconciliation As A Key To Enable Digitalisation Of Agrifood Industrial Processes: A Preliminary Case Study | ||||
| International Journal of Industry and Sustainable Development | ||||
| Volume 4, Issue 1, 2023, Page 41-52 PDF (726.88 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijisd.2023.311435 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Andrea Galeazzi; Francesca Girotto ; Lorenzo Rizzoli; Kristiano Prifti; Flavio Manenti; Laura Piazza
| ||||
| Abstract | ||||
| Through the adoption of the industry 4.0 matrix technology, the Agrifood 4.0 era calls for more big data to be collected and analyzed to help the transition from the traditional industry towards a digital twin-based and energy-saving industry. However, proper handling of those diverse information remains challenging. The often-overlooked prerequisite to digitalization is to verify the reliability of such data. Measured material streams can be validated by performing data reconciliation, a technique that uses material and conservation equations to minimize the actual measurement error in-process data. An application example is here proposed in order to provide an understandable guideline of data reconciliation procedure at a food industry level. Data reconciliation was, indeed, applied to the measured production data (material flows) of an industrial orange juice production line processing fresh raw material into concentrated juice (up to the desired dissolved sugar content of 65 °Bx). Energetic data assessment was performed using Aspen HYSYS v11.0. Results proved the importance of getting reconciled data in order to close material balances clearing the measurement inaccuracies due to operators’ errors or sensors bias. Besides, through Aspen HYSYS software, the surplus to requirements of the steam applied in the concentration unit operation was pointed out. The main perspective for future research will be the application of the present outputs to create the digital twin through which material and energy flows can be simulated to identify the best process optimization strategy. | ||||
| Keywords | ||||
| Agrifood 4.0; process optimization; digitalization; data reconciliation; orange juice production line | ||||
|
Statistics Article View: 211 PDF Download: 251 |
||||
