PTQ Q2 2023 Issue

improved operation of existing equipment. The second step is to reuse water reclaimed as make-up without any change to its quality. The third step is to recycle reclaimed water as make-up after quality improvement. With each step, the financial investment, the level of complexity, and the use of water treatment resources increase. As water scarcity contin- ues to increase and water quality continues to decrease, more refineries are evaluating recycle projects to meet their water conservation and performance goals. Used water or wastewater has a different quality than fresh water in terms of total solid content, total organic carbon concentra- tion, concentration of salts and metals, and microbiological species. Poor water quality can reduce functionality or cause severe damage, such as reduced heat transfer by biofilm or inorganic scale in the heat exchanger or microbiologi- cally induced corrosion. Innovative, high-performance and flexible chemistries, together with advanced performance- based monitoring and control tools, are becoming essential for water treatment companies partnering with refineries to meet their water conservation goals, especially when recy- cling water. A comprehensive wastewater treatment pro- gramme can minimise the size of investment in a recycling project that ensures good feedwater quality. Q To what extent do you see the deployment of digital approaches to maintain and operate facilities while lever- aging artificial intelligence (AI) in the restructured opera - tions of the petrochemical industry? A Mike Aylott, Chief Technology Officer, Rick Lucas, Principal Consultant, Geannie Gardner, Global Digital & Asset Transformation Solution Leader, KBC, geannie. gardner@kbc.global Digital technologies are significant enablers of smart manu - facturing throughout petrochemical enterprises, from plant floor to boardroom. Consider some examples: • Looking from the bottom up IIoT sensors, coupled with robotics, drone technology, and AI/ML, enable plant man- agers to change how rotating equipment is monitored and maintained. Now, operators are freed from daily inspection rounds because the sensors plus AI flag early warnings of trouble, allowing robotics to guide visual inspections. Reliability improves with continual monitoring. Combining predictive AI algorithms with better data directs mainte- nance efforts to where it is needed, which leads to fewer unplanned shutdowns. • Operator roles change Additional sensor information and AI guidance are accessible via dashboards and 3D visualisations, readily available simulations (first principles, ML-based or hybrid), plant knowledge bases, and so on. This data provides rich insight into plant performance. Therefore, operators work with true digital twins of their plant, and their role evolves to monitoring, reviewing, and approving the outputs of the various AI/ML applications. • More flexibility to operations scheduling AI-driven algo- rithms married to new analytic techniques can be applied in

plant control systems, allowing for faster switches between product grades. • Looking top-down Digitalisation gives organisations consolidated access to information, cutting through tradi- tional silos. This, in turn, allows more automated workflows and hence increased business agility. For example, common scenarios around supply and demand opportunities can be examined by AI-enabled workflows, with planners and schedulers reviewing recommendations rather than run- ning the analyses themselves. Accepted recommendations can be implemented automatically from the ERP system to plant floor, increasing responsiveness. • Travelling the industrial autonomy journey The oppor- tunity to be fully achieved involves digitising the human experience and knowledge, then coding this data to analyse key decisions, assess the effectiveness of human machine interfaces, and capture key learnings. Manufacturers that embrace these rapidly developing digital technologies and techniques, including AI/ML, are likely to survive and thrive in this volatility, uncertainty, complexity, and ambiguity (VUCA) world. As a result, they should be able to operate more nimbly, with more empowered workforces and report greater returns on capital. A Andrew Ledlie, Global Director Digitalization Strategies, Solenis, aledlie@solenis.com Refineries within the petrochemicals industry are increas - ingly employing digital technologies, including AI, that sup- port their water treatment management efforts to achieve stricter sustainability targets for reducing water use and improving energy efficiency. This trend, because of the use - fulness of these technologies, is likely to continue. The latest digital approaches to water treatment in refin - eries leverage three key areas: instrumentation, remote monitoring, and predictive analytics using AI. AI is becoming a key tool for predicting at an early stage the scaling, cor- rosion, and fouling tendency within cooling water systems. In terms of instrumentation, many innovative devices, including sensors, analysers, and controllers, have been developed in recent years. For example, Solenis developed a patented analyser that employs ultrasound to measure accurately fouling and deposition in situ. Consequently, when the analyser is used to measure fouling in heat exchangers, the heat exchangers do not need to be opened as frequently for inspection because the ultrasound device gives a real-time in situ picture of any fouling. Remote monitoring is a powerful way to provide all key stakeholders instant access to critical information in real- time. This enables faster troubleshooting of emerging prob- lems. Waiting for plant personnel to assemble and provide data or for experts to visit the site is costly when downtime or extended production slowdowns occur. Use of a trusted cloud platform, such as Solenis Cloud, addresses this need and allows all stakeholders to see the flow of problem resolu - tion remotely in real-time, thereby reducing stress and pro- viding peace of mind. This platform uses statistical process control tools and techniques to process and display data, thus enabling refinery operators to easily monitor and opti - mise the performance of their water treatment programmes.

PTQ Q2 2023