From process and materials development to maintenance and logistics, artificial intelligence (AI) is emerging as a transformative force across the chemical process industries
From process and materials development to maintenance and logistics, artificial intelligence (AI) is emerging as a transformative force across the chemical process industries.
As in many other sectors, artificial intelligence (AI) technologies are beginning to emerge in the chemical process industries (CPI). While AI-assisted solutions, and other associated technologies, such as robotic process automation (RPA), Internet of Things (IoT), automated drones and quantum computing, are still relatively new for many CPI applications, developers and users alike are realizing their potential benefits for expediting research and development (R&D), predictive maintenance, process optimization and more.
Global data integration
Within its Smart Operations initiative, Henkel AG & Co. KGaA (Düsseldorf, Germany; www.henkel.com ) is utilizing AI capabilities in its global process operations and supply chain. “We use AI to run efficient analyses of complex data arrays for achieving higher production performance, quick product innovation and scaleup for our self-adjusting production systems,” explains Sandeep Sreekumar, global head of Adhesive Digital Operations at Henkel.
“Our focus is not only on collecting internal manufacturing data, but also on actively working with customers on data collection opportunities during product usage to make improvements and adjust to changing customer needs,” says Sreekumar. Henkel currently applies externally built AI technologies, but the company envisions creating an ecosystem where both internal and third-party solutions co-exist and establishing a fully transparent global supply-chain and operations network that is both automated and self-adjusting to variability, explains Tim Gudszend, global head of Adhesive Technologies and Investment at Henkel.
The company’s “smart factory” technologies are designed to enhance understanding of raw materials availability and current production status to better advise operations personnel on how to adjust the production process to improve performance. “By analyzing these data, we have implemented significant raw-material yield improvements and increased performance quality within these plants,” adds Gudszend.
While Henkel has seen success in its AI projects, any implementation of new technologies is not without its challenges. “One of the biggest issues is generating all of the relevant data for a process and its influencing environment, and making this information available for a ‘big data’ solution so it can be utilized to its full extent,” explains Gudszend, adding that Henkel is deploying enhanced data-analytics platforms to better integrate data across its global supply-chain and operations networks. Even with the challenges, Sreekumar emphasizes that Henkel has realized many AI benefits, from expediting speed to market for new product formulations and scaleup to rapidly detecting and resolving product-quality issues.
“AI technologies are disruptive, and will continue to help drive new product launches and improve production rates from months and years to weeks or days. The technologies will encourage the development of new business models, improve operating conditions and generate better-quality products,” he continues.[…]