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CHEMICAL INUSTRY Disruptive digital technologies in the chemical industry

Jul 5, 2021

Narasimha Kamath, VP - Manufacturing, Tata Chemicals - The Indian chemical industry has been a major contributor to the country’s GDP. While digital transformation has been rampant in the manufacturing industry, the chemicals industry would also reap significant benefits by implementing it across its factory units. The article by Tata Chemicals – which operates basic chemistry and specialty chemistry – studies the breakthroughs various digital technologies can bring in the chemicals sector.

The Indian chemical industry is one of the major contributors to the Indian economy, contributing 7% of the country’s GDP and is expected to reach $304 billion by 2025, registering a CAGR of 9.3%. It contributes to almost every manufactured product and serves as the backbone of many end-market industries, such as agriculture, automotive, construction and pharmaceuticals. Not to mention, the digital transformation in the chemical industry can’t be ignored, as it has potentially transformed the chemical industry by promoting strategic growth and streamlining operations through automation and digitisation, and there is a long way to go.

The chemical industry has witnessed a new wave of digitisation in this COVID-19 era, as various digital technologies are being rapidly adopted by chemical companies in the hope of driving innovation, profitability and staying ahead within the industry. Today, the companies require real-time data that allows chemical providers to run their businesses in an online world as well. Automation has greatly helped in cutting down on inefficiencies across the manufacturing and chemical sectors alike. Let us now look at some of the disruptive digital technologies that have brought breakthroughs in the chemical industry globally.

Industrial Internet of Things (IIoT)

Digital technologies, like IIoT, provide an enhanced insight on operations and capabilities of production systems, enabling greater visibility on status and integration as well as a deep exploration of alternatives to aid operational decision making. With the advent of IIoT, there is a further scope to accelerate operational excellence in the manufacturing areas.

One of the key advantages of IIoT is that it provides actionable insights to the key stakeholders, which help them take proactive decisions. Data-driven actionable insights help make operations more predictable and avoid surprises. This has a positive impact on efficiency, yield, uptime and other critical KPIs. But IIoT, an untapped source in the chemical industry, can greatly benefit the chemical companies in ensuring a competitive edge, safety and compliance regulations in the chemical industry. Moreover, IIoT delivers significant efficiency improvements to production processes. In the chemical industry, IIoT can serve as a base platform to create an intelligent network of devices that can interrelate data and processes to effectively establish feedback control systems within the context of industrial automation, which will further enhance interconnectivity, versatility, scalability, time efficiency, cost-effectiveness, security, productivity and operational efficiency.

Digital twins

Digital twins are yet another digital technology that can help train operators in atypical operations, especially in start-ups, shutdowns, slowdowns and other unexpected events. Such training is crucial to avoid potentially dangerous situations that can further lead to environmental incidents.

Plant level digital twins greatly help in the optimisation of potential manufacturing conditions, suggesting decreased production rates and alternate raw material environments, which ultimately adds to the readiness of several industries to face any crisis. Moreover, a digital twin would also enable chemical operators to create a unique environment to look at all their complex processes and suggest optimum solutions.

Data analytics

The use of data science and analytics has increased significantly in the chemical industry. The industry is quickly moving towards automation as it is an evergreen field that finds its use in every industry. There are a lot of recording failures in the chemical industry, errors in parameter recording that can sabotage various simulations and processes. In such situations, data science and analytics come to the rescue by providing immediate solutions, such as assisting in the rapid identification of trends and patterns, an absolute necessity in the case chemical industry to recheck a finding and reducing human effort, which means fewer errors and lower costs. Data analytics also makes it possible to achieve proficiency in a complex & unpredictable setting due to its ability to deal with multi-dimensional and multi-variety data efficiently.

Data science and analytics can further help chemical companies analyse their logs and look at the top risks that can emerge out of the data. Thus, giving us predictability, helping us take action and lessen the chances of such incidents happening.

Artificial Intelligence

Chemical industries are currently confronted with several difficult issues, including high energy use, hazardous risk assessment and environmental policy, which compel the industry and research institutions to develop new technologies, catalysts and materials. Process management techniques currently in use are incapable of dealing with dynamic situations in which process dynamics can change at any time due to organisational changes.

Machine Learning and Artificial Intelligence (AI) are two powerful advances in computer science engineering technology that can offer a plethora of benefits to the chemical industry. They can help achieve strong catalysts, precise control over the running processes and optimum planning & operation schemes possible.

Deep learning, a great AI technique, aids in recognising activity modes, detecting faults and analysing risks in the petroleum refining processes. Deep learning can be very useful in training the reactors to maintain sufficient product concentration and flow using historical data on inflow, concentration, liquid level and outflow. When compared to conventional control loops, Big Data and AI can provide better process control. Statistical machine learning and evolutionary computation can prove critical for the characterisation of petroleum products, chemical change modelling, process optimisation, decision-making, environmental understanding and automated troubleshooting of a wide range of problems in the chemical industry.

Cybersecurity

Chemical sites and related infrastructures, where significant amounts of hazardous materials are manufactured, stored or transported, are increasingly vulnerable to security threats. And, as a result of the lockdown and social distancing steps introduced during the coronavirus pandemic, the shift to more widespread home working in a compressed timeframe has spread company networks wider than they have ever been, exposing a slew of vulnerabilities that hackers are looking to exploit.

Cybersecurity aids in protecting computer networks, computers, and other electronic assets against data breaches, cyber-attacks, and/or unauthorised access. Cybersecurity uses a variety of approaches borrowed from various disciplines such as computer science, criminology and cryptology to ensure that an organisation’s data and properties are protected from hackers, cybercriminals and other malicious agents.

Cyber-attacks on a chemical plant may take two forms – it could be an intelligence activity to gain access to intellectual property, such as formulations or process flow diagrams, or an attack on the ICS, which controls vital plant functions, and a failure caused by a well-planned attack could result in physical harm. Chemical industries deal in the storage and processing of operationally hazardous chemicals like chlorine, hydrogen chloride, nitric acid, ammonia, vinyl chloride and methyl isocyanate, among various other harmful grade chemicals. Not to mention, having a strong cybersecurity framework is a necessity that cannot be overlooked to maintain safety at the workplace.

Predictive analytics

Predictive analytics is increasingly seen as the panacea to address chemical industries’ pain points. This AI-powered technique uses historical and real-time data to predict critical future outcomes, reduce risks, improve operations, cut costs and increase revenue. Chemical manufacturers can increase the operating time of critical assets by using predictive analytics to find ways to anticipate their failure.

Predictive maintenance analyses the historical performance data and offers real-time data of production units and their machinery to forecast when equipment is likely to fail, limits the time it is out of service and identifies the root cause of the problem. Yield, energy and throughput analytics can be used to ensure that the individual production units are as efficient as possible when they are operating.

The path ahead

As we advance, automation and digitisation technologies like IIoTs and analytics are going to play a huge role in the chemical industry. As with any major initiative, digital transformation in the chemical industry is likely to be challenging. But with the help of the right framework and vision, chemical companies can increase their manufacturing process efficiency and can help them negate human errors. Thus, avoiding any mishap in the chemical industry.

Image Gallery

  • Plant level digital twins greatly help in the optimisation of potential manufacturing conditions, suggesting decreased production rates and alternate raw material environments, which ultimately adds to the readiness of several industries to face any crisis

  • Statistical machine learning and evolutionary computation can prove critical for the characterisation of petroleum products, chemical change modelling, process optimisation, decision-making, environmental understanding and automated troubleshooting of a wide range of problems in the chemical industry

  • Narasimha Kamath

    VP - Manufacturing

    Tata Chemicals

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