Industrial Chemistry
Open Access

Data-driven fault detection; Chemical process optimization; Industrial safety; Efficiency enhancement; Sustainable manufacturing

Introduction

In the realm of industrial chemistry, ensuring the safety, efficiency, and sustainability of chemical processes is paramount. The seamless operation of chemical plants requires vigilant monitoring and prompt identification of any deviations from normal operation that could potentially lead to faults or inefficiencies [1]. Traditionally, fault detection and diagnosis (FDD) relied on empirical observations and simplistic rule-based systems, often struggling to capture subtle deviations amidst the complexities of modern industrial environments. However, a paradigm shift is underway with the advent of advanced data-driven techniques in fault detection and diagnosis [2,3]. By harnessing the power of big data, machine learning, and artificial intelligence, industrial facilities are revolutionizing their approach to FDD, unlocking unprecedented insights and capabilities. This paper explores the transformative impact of advanced data-driven FDD on industrial safety and efficiency, shedding light on its key advantages, challenges, and implications for the field of industrial chemistry [4,5]. Through the integration of sophisticated data analytics techniques with the intricate workings of chemical processes, advanced data-driven FDD offers a holistic approach to monitoring and optimization. Sensors, actuators, and other monitoring devices embedded within chemical plants generate vast amounts of data, which serve as the raw material for predictive analytics algorithms [6]. By analyzing these data streams in real-time, statistical models, pattern recognition algorithms, and anomaly detection techniques can identify subtle deviations from normal operating conditions, enabling preemptive maintenance actions and mitigating the risk of catastrophic failures [7,8]. Moreover, advanced data-driven FDD facilitates root cause analysis by correlating disparate data points and uncovering hidden relationships within the process. By tracing the origins of faults back to their source, engineers can implement targeted interventions to address underlying issues and prevent recurrence. This proactive approach not only minimizes downtime but also enhances the overall reliability and resilience of chemical processes. Furthermore, the scalability and adaptability of advanced data-driven FDD make it well-suited for the complexities of modern industrial environments [9]. Whether applied to batch processes, continuous operations, or hybrid systems, these techniques can be tailored to suit the unique characteristics of each process. Additionally, as chemical plants evolve and adapt to changing market demands, the flexibility of data-driven FDD ensures that it remains a valuable asset throughout the lifecycle of the facility. In the pursuit of sustainability, advanced data-driven FDD plays a pivotal role in optimizing resource utilization and minimizing environmental impact [10]. By identifying inefficiencies, optimizing operating parameters, and reducing waste generation, these techniques contribute to the overarching goal of sustainable manufacturing. Furthermore, by enabling early detection of leaks, emissions, or other environmental hazards, advanced FDD helps safeguard both human health and the natural world. However, despite its transformative potential, the adoption of advanced data-driven FDD is not without its challenges. Integration with legacy systems, data quality issues, and cybersecurity concerns are just a few of the hurdles that must be overcome. Additionally, the need for domain expertise in both chemistry and data science underscores the importance of interdisciplinary collaboration in realizing the full benefits of these technologies.

Conclusion

Advanced data-driven fault detection and diagnosis (FDD) stand as a transformative force in the realm of industrial chemistry, revolutionizing the way chemical processes are monitored, optimized, and managed. Through the integration of sophisticated data analytics techniques with the intricate workings of chemical plants, advanced FDD offers unprecedented capabilities in enhancing industrial safety, efficiency, and sustainability. By harnessing the power of big data, machine learning, and artificial intelligence, industrial facilities can detect subtle deviations from normal operation in real-time, enabling preemptive maintenance actions and mitigating the risk of catastrophic failures. Moreover, advanced FDD facilitates root cause analysis, empowering engineers to address underlying issues and prevent recurrence, thereby enhancing the reliability and resilience of chemical processes. The scalability and adaptability of advanced data-driven FDD make it well-suited for the complexities of modern industrial environments, allowing it to be tailored to suit the unique characteristics of each process. Furthermore, its role in promoting sustainability by optimizing resource utilization, minimizing environmental impact, and safeguarding human health cannot be overstated. However, the adoption of advanced data-driven FDD is not without its challenges, including integration with legacy systems, data quality issues, and the need for interdisciplinary expertise. Overcoming these hurdles will require collaboration between experts in chemistry, data science, and industrial engineering.

References

1. Douros A, Renoux C, Yin H, Filion KB, Suissa S, et al. ( 2017) Concomitant use of direct oral anticoagulants with antiplatelet agents and the risk of major bleeding in patients with nonvalvular atrial fibrillation Am J Med 132 : 191-199.

Indexed at, Google Scholar, Crossref

2. Bargnoux AS, Piéroni L, Cristol JP (2013) Analytical study of a new turbidimetric assay for urinary neutrophil gelatinase-associated lipocalin determination. Clin Chem Lab Med 51: 293-296.

Google Scholar, Crossref, Indexed at

3. Westhoff JH, Tönshoff B, Waldherr S (2015) Urinary tissue inhibitor of metalloproteinase-2 insulin-like growth factor-binding protein 7 predicts adverse outcome in pediatric acute kidney injury. Plos One 10: 143-628.

Google Scholar, Crossref, Indexed at

4. Gillespie IMM, Philip JC (2013)Bioremediation an environmental remediation technology for the bioeconomy.Trends Biotechnol31: 329-332.

Indexed at, Google Scholar, Crossref

5. Anand S, Mande SS (2018)Diet, Microbiota and gut-lung connection. Front Microbiol 9: 21-47.

Indexed at, Google Scholar, Crossref

6. Barolo MI, Ruiz Mostacero N, caricaL (2014) An ancient source of food and health.Food Chem 164: 119–127.

Indexed at, Google Scholar, Crossref

7. Ala-Korpela M. (2019) The culprit is the carrier, not the loads: cholesterol, triglycerides and Apo lipoprotein B in atherosclerosis and coronary heart disease. Int J Epidemiol 48: 1389-1392.

Indexed at, Google Scholar, Crossref

8. Esper RJ, Nordaby RA (2019) cardiovascular events, diabetes and guidelines: the virtue of simplicity. Cardiovasc Diabetol 18: 42.

Indexed at, Google Scholar, Crossref

9. Lorentzen HF, Benfield T, Stisen S, Rahbek C (2020) COVID-19 is possibly a consequence of the anthropogenic biodiversity crisis and climate changes.Dan Med J67: 20-25.

Indexed at, Google Scholar

10. Abrahamsson TR, Jakobsson HE, Andersson AF, Bjorksten B, Engstrand L, et al. (2014)Low gut Microbiota diversity in early infancy precedes asthma at school age. Clin Exp Allergy 44: 842-850.

Indexed at, Google Scholar, Crossref