7 reasons to implement AI in your chemical manufacturing plant

Featured article by Chen Linchevski

Like other process manufacturing verticals, chemical plants are under immense pressure today. COVID-19 forced many into a hasty digital transformation that they are still struggling to complete and consolidate, while it also disrupted supply chains and transformed market demands. Many pharmaceutical companies that are involved in the current COVID-19 vaccine drive rely on components from chemical plants, further raising the stakes for organizations that form their supply chain.

Fortunately, at the same time as business conditions are becoming more challenging, tech is evolving to help companies to bear the strain. Artificial intelligence (AI), machine learning (ML), deep learning (DL), and big data from IIoT sensors and connected devices have matured to the point where they are accessible, affordable, and impactful for the average chemical plant. Here are 7 ways that AI can help chemical manufacturing plants.

1.   Reduce repair and maintenance costs

AI supports predictive maintenance which can detect the earliest signs of potential part failure, producing targeted alerts that help process engineers quickly identify the cause of the problem. With an earlier warning about potential incidents, maintenance teams have the luxury of making a considered decision about whether to replace a part now, or carry out a quick repair that will keep everything going until the next scheduled down time. Cleaning out a fouled system after receiving an early alert, for example, can prevent part failure later in an item that’s expensive and hard-to-fix. Equipment that’s at peak operating efficiency also reduces raw material and energy wastage; no small issue when energy can make up half the costs of chemical processing.

Early intervention means that maintenance teams can often fix a part before it breaks entirely, thereby extending part lifecycle and saving money in replacement parts. Additionally, if these small failures went undetected, they often snowball into much larger events which force the plant to close down entirely for hours or even days while maintenance teams carry out a long, complex, and expensive repair and replacement project, thereby adding the cost of lost production to the bill.

2.   Improve product quality

AI-powered early warnings go beyond predictive maintenance to include predictive monitoring. Predictive monitoring uses machine learning (ML) and big data to spot anomalies anywhere in the plant, which could indicate inefficiencies within the process.

Sometimes these are due to part failure, but at other times they reveal a faulty sensor, a blockage, or a fouled pipe that’s preventing the system from operating at peak efficiency. By quickly identifying and clearing such issues, plant engineers can ensure that both product quality and quantity consistently meet expectations.

3.   Act instead of reacting

Plants that had already completed a digital transformation and implemented AI-based solutions within the plant were far better able to weather the pandemic than those which still relied on manual processes. Their ML systems are able to collect the multitude of data from IoT sensors and connected devices, mine those datasets for patterns and trends, and produce accurate alerts about bottlenecks in workflows, inefficiencies in processes, and/or impending failures.

Process engineers are then able to make calm, informed decisions about maintenance, repairs, and process changes from a place of knowledge and insight, rather than reacting hastily to one unexpected incident after another. Stress and anxiety cause “tunnel vision” that leads to poor decision-making, harming the smooth running of your plant.

4.   Switch your focus to strategic planning

Besides helping plant engineers move past the “chasing fires” mode, AI-based solutions also support executives in their strategic planning. ML modeling can help deliver predictions about the health of your supply chain, potential market changes, fluctuations in consumer demand, even the spread of seasonal colds around the factory floor so that you can adjust employee rotas accordings. That’s setting aside how it can help you rearrange employee scheduling, positioning, and movement around the plant to reduce the spread of COVID-19.

AI-generated forecasts allow chemical executives to spot and mitigate emerging risks and identify and seize nascent opportunities that can improve the profitability of the organization as a whole. With the help of AI solutions, chemical plant managers can make strategic decisions that maximize plant output and efficiency, instead of making hasty choices on the back foot.

5.   Increase employee safety

Chemical plants are high-risk locations, full of hazardous materials and dangerous processes. Chemical plants must comply with complex traceability and accountability regulations and need AI to capture, store, and deliver data specific materials, products, and processes in the event of an audit. It’s crucial to keep your employees safe, but you also need them to carry out important actions, handle hazardous materials, and operate equipment which could place them in harm’s way. Piping, for example, is one of the main causes of accidents in a chemical plant, and human (rather than technical) issues are the primary cause of such incidents.

AI and ML power smart machines which can automate risky processes so that you don’t need to place employees in potentially dangerous positions. Connected devices collect and share data so that humans don’t have to enter toxic environments to check on equipment health, while early alerts from predictive maintenance solutions help ensure that every item in the plant is in full working order, reducing the risks of dangerous incidents.

Additionally, predictive monitoring solutions that detect tiny anomalies and inefficiencies help lower the risks of leakage, spillage of hazardous materials, and industrial flares, all of which can harm human health and damage the local environment. Fines for non-compliance with environmental regulations begin at $12,600 per violation and rise to $127,000, but the damage it does to your reputation can be priceless.

6.   Prepare to meet fluctuating customer demand

In the past few years, thanks to the trend of personalization across every vertical and particularly that of personalized medicine, chemical plants have had to cope with small-batch, customized orders, with the particulars often changing at the last minute. Additionally, COVID-19 brought a lot of changes to the pharma industry and the demands it makes on the chemical industry for components for medications and treatments.

AI and ML-based models can produce reliable predictions that help executives to forecast trends in demand within their market, as well as opening up visibility into supply chains. Chemical supply chains tend to be complex, stretching across multiple continents and suppliers. If any link fails, it affects production and reduces the plant’s ability to compete with rival companies. With AI, chemical companies can anticipate and prepare to meet new demands while they are still on the horizon, while ensuring that supply chains remain robust and reliable enough to support any changes in their raw material needs.

7.   Run a more resilient plant

During lockdown orders and periods of high local infection rates, plants are forced to operate with a skeleton staff. Errors and inefficiencies which would normally be picked up by alert employees go unnoticed when those employees are working from home, while other processes risk disruption because they rely on manual action.

Predictive maintenance and predictive monitoring ensure that small failures, mistakes, and inefficiencies don’t multiply, unseen, until plant management is facing a catastrophic concatenation of errors that forces days of downtime and a massive repair bill. AI automation and smart machines can complete long processes efficiently under remote supervision, enabling a connected plant to continue smoothly meeting production targets no matter what happens.

AI use is a major differentiator for today’s chemical plants

Chemical plants today can apply AI and ML to create smart factories, self-learning processes, and reliable forecasts that assist organizations to mitigate risk and seize opportunities, reduce costs, improve product quantity, cut downtime, raise employee and environment safety levels, and improve their resilience and agility to meet the difficulties of today’s chemical market.

Author Bio

Chen Linchevski, a true believer in the ability of the industrial sector to transform itself and increase efficiency, took raw ideas and transformed them into promising companies. Throughout the execution Chen demonstrated true leadership and vision. Precognize, of which he’s CEO today, was nominated by the World Economic Forum as Tech Pioneer of 2018 and acquired by Samson Group. During his career, Chen worked with key industrial companies such as GE, BASF, Shell, and LG, specializing in the design and analysis of complex industrial systems.