Digitalisation and use of AI/ML in Indian chemical industry – advantages and shortcomings: Shrey B Patel, Founder, Angiras Rasayan
Opinion

Digitalisation and use of AI/ML in Indian chemical industry – advantages and shortcomings: Shrey B Patel, Founder, Angiras Rasayan

Several Indian chemical companies have successfully implemented digitalisation and AI/ML, showcasing the transformative potential of these technologies

  • By Shrey B Patel , Founder, Angiras Rasayan | August 02, 2024

The chemical industry, often viewed as the cornerstone of modern industrialisation, has undergone a dramatic transformation with the rise of digitalisation and the integration of Artificial Intelligence (AI) and Machine Learning (ML). In India, this evolution marks more than just a trend; it represents a fundamental shift poised to enhance efficiency, safety, and innovation. Let's delve into the significant impact of digitalisation and AI/ML on India's chemical sector, highlighting its benefits and challenges.

Emergence of Digitalisation in the Chemical Industry

Digitalisation involves adopting digital technologies to modify business models, optimise processes, and boost productivity. In the chemical industry, digitalisation spans a wide range of technologies, including the Internet of Things (IoT), big data analytics, cloud computing, and AI/ML.

India's chemical industry, which holds a vital position in the global market, is leveraging these technologies to tackle traditional challenges such as operational inefficiencies, safety issues, and environmental sustainability. The integration of AI and ML is particularly proving to be revolutionary.

Role of AI and ML in the Chemical Industry

AI and ML are components of digitalisation that offer distinct advantages through predictive analytics, process optimisation, and enhanced safety protocols. These technologies use vast amounts of data generated in chemical processes to deliver actionable insights, predictive maintenance, and automation.

➢ Predictive Analytics and Process Optimisation

One of the primary benefits of AI/ML in the chemical industry is predictive analytics. By analysing historical data, AI algorithms can forecast potential equipment failures, process anomalies, and maintenance requirements. This predictive capability helps reduce downtime, optimise resource allocation, and prevent catastrophic failures.

For example, a chemical plant with AI-driven predictive maintenance can predict a pump failure, weeks in advance. This allows for timely intervention, avoiding unplanned shutdowns and saving significant costs. Additionally, ML models can continuously learn from operational data to refine processes, thereby enhancing yield, reducing waste, and conserving energy.  According to a report by the International Society of Automation, predictive maintenance can reduce downtime by up to 50% and extend the lifespan of machinery by 20%.

➢ Enhanced Safety Protocols

Safety is crucial in the chemical industry, where hazardous materials and complex processes present significant risks. AI and ML can greatly enhance safety protocols through real-time monitoring, risk assessment, and automated control systems. Several open literature sources and guidebooks have recently been published highlighting the safe use of AI/ML for safety. One such guidebook, "The Application of Artificial Intelligence in Functional Safety" by IET, UK, underscores the vital role of AI in functional safety. It highlights how AI systems can process data from multiple sensors to detect unsafe conditions instantly and trigger appropriate responses. For instance, an AI system can monitor pressure levels, temperature, and chemical concentrations in real-time, identifying deviations that might indicate a potential hazard. Such systems can automatically shut down processes or activate emergency measures, thereby preventing accidents and ensuring worker safety.

Advantages of Digitalisation and AI/ML

➢ Increased Efficiency and Productivity

The integration of digital technologies, AI, and ML has led to substantial improvements in efficiency and productivity. Automated systems and smart algorithms optimise production schedules, manage supply chains, and streamline operations. These advancements result in higher throughput, lower operational costs, and better resource utilisation. A study by McKinsey & Company estimates that digitalisation can boost productivity in the chemical industry by 15- 20%.

➢ Improved Quality Control

AI-powered quality control systems can inspect products at various production stages, ensuring adherence to stringent quality standards. Machine vision systems, for example, can detect defects with greater accuracy and speed than human inspectors. This not only enhances product quality but also reduces the likelihood of recalls and rework, which can cost companies millions of rupees annually.

➢ Sustainable Practices

The chemical industry faces increasing pressure to adopt sustainable practices. Digitalisation and AI/ML facilitate this transition by enabling more efficient use of raw materials, reducing energy consumption, and minimising waste. Advanced analytics can optimise reaction conditions to maximise yield and reduce the generation of hazardous by-products. The Confederation of Indian Industry (CII) has reported that AI-driven optimisation can reduce energy consumption in chemical plants by up to 10%.

➢ Enhanced Decision-Making

AI and ML provide chemical industry leaders with valuable insights derived from data analytics.  These insights inform strategic decisions, such as process improvements, market trends, and investment opportunities. The ability to make data-driven decisions enhances competitiveness and resilience in a dynamic market.

Shortcomings and Challenges

While the advantages of digitalisation and AI/ML are significant, the Indian chemical industry faces several challenges in fully realising their potential.

➢ High Initial Investment

Implementing digitalisation and AI/ML solutions requires considerable capital investment. The cost of advanced technologies, skilled personnel, and infrastructure can be prohibitive, particularly for small and medium-sized enterprises (SMEs). This financial barrier can slow the adoption of these technologies. The National Association of Software and Service Companies (NASSCOM) has noted that SMEs often struggle with the high costs associated with digital transformation.

➢ Data Privacy and Security Concerns

The reliance on data for AI/ML systems raises concerns about data privacy and security. The chemical industry handles sensitive information, including proprietary processes and formulas.  Ensuring the protection of this data from cyber threats is paramount. Robust cybersecurity measures and compliance with data protection regulations are essential to mitigate these risks.

➢ Skill Gap

The successful implementation of digitalisation and AI/ML requires a workforce with specialised skills in data science, AI, and digital technologies. However, there is a noticeable skill gap in the Indian chemical industry. Bridging this gap through training, education, and collaboration with academic institutions is crucial for leveraging the full potential of these technologies.  According to a report by the Indian Ministry of Skill Development and Entrepreneurship, the chemical industry needs to upskill nearly 50,000 workers to meet future demands.

➢ Integration Challenges

Integrating digital technologies with existing systems and processes can be complex. Legacy systems, lack of standardisation, and interoperability issues pose significant hurdles. A well planned integration strategy, supported by technical expertise, is necessary to overcome these challenges.

Success Stories from India's Chemical Industry

Several Indian chemical companies have successfully implemented digitalisation and AI/ML, showcasing the transformative potential of these technologies.

➢ Reliance Industries Limited (RIL)

Reliance Industries Limited, a conglomerate with a significant presence in the chemical sector, has been at the forefront of digitalisation. RIL has implemented AI-driven predictive maintenance across its refineries and petrochemical plants. By leveraging real-time data and machine learning algorithms, RIL has achieved a substantial reduction in unplanned downtime and maintenance costs.

➢ Tata Chemicals

Tata Chemicals has embraced digitalisation to enhance its manufacturing processes. The company uses IoT sensors and AI analytics to monitor and optimise production in real-time.  This has resulted in improved operational efficiency, reduced energy consumption, and enhanced product quality. Tata Chemicals' commitment to sustainability is further bolstered by AI-driven initiatives aimed at minimising environmental impact.

Future of Digitalisation and AI/ML in India's Chemical Industry

The journey of digitalisation and AI/ML in India's chemical industry is just beginning. The future holds immense potential for further advancements and innovations.

➢ Autonomous Chemical Plants

The concept of autonomous chemical plants, where AI systems control and optimise all aspects of production, is gaining traction. These plants would operate with minimal human intervention, relying on AI for decision-making, process control, and safety management. The benefits include increased efficiency, reduced human error, and enhanced safety.

➢ Digital Twins

Digital twin technology, which involves creating a virtual replica of a physical system, is set to revolutionise the chemical industry. Digital twins enable real-time monitoring, simulation, and optimisation of chemical processes. This technology allows for predictive maintenance, process optimisation, and scenario analysis, leading to improved performance and reduced operational risks.

➢ Collaboration and Innovation

The future success of digitalisation and AI/ML in the chemical industry will depend on collaboration between industry stakeholders, academia, and technology providers. Open innovation platforms, joint research initiatives, and knowledge sharing will accelerate the development and adoption of these technologies.

Conclusion

Digitalisation and the integration of AI/ML are transforming the chemical industry in India, offering numerous advantages such as increased efficiency, enhanced safety, and improved sustainability.  However, challenges such as high initial investment, data privacy concerns, and skill gaps must be addressed to fully harness the potential of these technologies.

As India's chemical industry continue its digital journey, the collaborative efforts of industry leaders, policymakers, and technology innovators will be pivotal in shaping a resilient and competitive future. By embracing digitalisation and AI/ML, India's chemical industry is not only poised to enhance its global standing but also to contribute significantly to the nation's economic growth and sustainable development. The transformative power of these technologies promises a future where innovation and efficiency go hand in hand, propelling India towards achieving the dream of Viksit Atmanirbhar Bharat.

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