(429e) Development and Implementation of Customized Symbols Based on the Greenscope Methodology for Sustainability Evaluation of Industrial Processes in the PI System

Industry 4.0 represents the fourth industrial revolution, which leverages advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics to optimize industrial processes. Unlike previous industrial revolutions, Industry 4.0 emphasizes the collection and analysis of vast amounts of real-time data, which enables precise and efficient decision-making (Kestering et al., 2023). The acquisition, automation, and digitalization of processes are crucial for organizations to stay competitive in this new era. One of the significant benefits of Industry 4.0 is the regulation of information flow throughout the production process. Real-time data collection enables the identification of deviations, implementation of modifications/customizations, and ensuring the quality of the final output. With the ability to monitor key variables and indicators, Statistical Process Control (SPC) is a powerful tool that can identify anomalies that may have an impact on product quality (Montgomery, 2016).

This work is a continuation of a study presented in AIChE 2022 (Bispo et al., 2022), in which key process indicators were implemented in the PI System, to be evaluated in customized symbols implemented in PI Vision. The objective now is to expand the set of indicators based on the GREENSCOPE methodology, which allows for the evaluation of the sustainability of production processes (Ruiz-Mercado et al., 2014). For this purpose, a sustainability scale has been proposed for each indicator, delimited by two scenarios: the best and the worst case. This nondimensional scale transforms indicator scores into discrete unidimensional values between the selected best target and worst-case scores, facilitating visualization and comparison of sustainability assessment results for each indicator in four areas (Ruiz-Mercado, 2011). The structure implemented in the PI System has allowed the monitoring of the sustainability scale using a customized radar plot, as well as online verification based on SPC techniques. The integration of the GREENSCOPE methodology with the monitoring and control tools of the PI System has allowed a comprehensive and integrated point of view on process sustainability, enabling more efficient and precise decision-making.

References

Kestering, D., Agbleze, S., Bispo, H., Lima, F. V. (2023), Model predictive control of power plant cycling using Industry 4.0 infrastructure, Digital Chemical Engineering, 7.

Montgomery, D. C. (2016). Introduction to Statistical Quality Control (7th ed.). John Wiley & Sons.

Ruiz-Mercado, G.; Smith, R. L.; Gonzalez (2014). GREENSCOPE: Technical User’s Guide. U. S. Environmental Protection Agency.

Bispo, H., Gadelha, E., Lima, F. V., Dantas, B., Almeida, N. A. B., Guerra, I. (2022). Development and Application of Customized Symbols in PI Vision for KPI Monitoring Based on the Greenscope Methodology. Proceedings of the AIChE Annual Meeting 2022, vol. 118 (1), pp. 2345-2356. DOI: 10.1016/j.proche.2022.03.045

Ruiz-Mercado, G. J. (2011). Sustainability Indicators for Chemical Processes: I. Taxonomy. Industrial & Engineering Chemistry Research.