A Kandasamy Sundaram, Distinguished Technologist & Lummus Fellow, kandasamy.sundaram@lummustech.com SAF is addressed from different angles. Plastics pyrolysis, tyre pyrolysis, and vegetable oils are a few examples. They all have different types of contaminants compared with fossil fuels. Some adsorbents are used to remove some contami- nants. However, they are not able to reduce the concentration significantly. Hydrotreating is required. Isoterra for vegetable oil uses hydrotreating. Plastic pyoil requires hydrotreating to reduce chlorides and nitrogen. For chemicals production, adsorbents meet the specification in some cases. A Ezequiel Vicent, Senior Application Engineer and Consulting Lead, OLI Systems The advent of renewable fuels has brought the necessity to change catalyst to treat the carboxylic groups in the fatty acids that make up vegetable oils (increased CO, CO₂ and H₂O production) as well as an increase in chlorides. In addi - tion to catalyst selection, unit engineers need to focus on the production of the byproducts from these reactions. We have seen an increase in NH₄Cl salt formation out of these feeds that can foul the feed-effluent exchangers at higher temperatures. The increase in water formation (up to five times larger than usual hydrocarbon feed) means the possibility of the salts that deposit in the feed-effluent exchangers getting wet increases dramatically. Engineers need to monitor the exchangers for the NH₄Cl formation temperature as well as the relative humidity increase due to increased water content. The engineer should note that at relative humidity greater than 10%, ammonium chloride salts will start to absorb water from the vapour stream. This can cause under- deposit corrosion and pitting in equipment and piping. The equipment most at risk for this type of corrosion is the feed- effluent heat exchangers and the piping up to the reactor effluent air coolers inlet wash water injection. In this case, operations will need to invest in monitoring tools (both software and hardware) that can help them cal - culate salt formation temperatures, water relative humidity, and sour water concentrations (especially bisulphide con- centration) to maintain static asset reliability. Q What role are AI systems expected to play when opti- mising plant-wide operations? A Isabelle Conso, Digital Innovation Director, Isabelle. Conso@axens.net, and Philippe Mege, Digital Services Factory Manager, Philippe.MEGE@axens.net, Axens AI is expected to play a significant role in optimising plant- wide operations. Here are some key roles and benefits that AI systems can provide in this context: u Safety and compliance: AI can monitor safety conditions in the plant and detect anomalies or potential hazards. It can also assist in compliance with regulatory requirements by ensuring that processes and products meet the neces- sary standards. v Process optimisation: AI can continuously analyse vast amounts of data from various sensors to uncover patterns in view of production process optimisation. It can also
produce soft sensors generated through surrogate models that will provide insights for adjustments of parameters such as temperature, pressure, and flow rates to maximise efficiency and product quality. w Predictive maintenance: AI can monitor equipment and machinery in real-time, analysing data from sensors to pre- dict when maintenance is needed. This can help prevent unplanned downtime and reduce maintenance costs. x Production scheduling: AI can create optimised pro - duction schedules that balance production efficiency with demand fluctuations and resource constraints. A Ezequiel Vicent, Senior Application Engineer and Consulting Lead, OLI Systems AI will play a major role in the optimisation of plant-wide operations both during steady-state times and during shut- downs and start-ups. There are many examples of how AI is being used today to optimise a plant, but the decision to go from an open system to a closed system is still a few years away< and the technology has not yet caught up. A prime example of AI being used in plant optimisation is in the area of energy and emissions management. There are energy optimisers that use first principles to look at the current energy status of the unit and are able to optimise fuel consumption and steam production while account- ing for combustion emissions to minimise the amount of energy needed for the steam demand. They will account for steam Cogen units and heat inte- gration. However, to predict future demand, AI models ‘learn’ where the peaks and valleys come in and are able to predict the input changes before they happen. This helps the energy optimiser capture changes more quickly and have additional energy savings. Another area where AI, or in this case a Machine Learning (ML) model, can make a big impact is in dynamic processes, like the start-up or shutdown of a unit. Consider a unit where a process upset occurs upstream, and a column needs to be quickly shut down with a precise sequence of events. The outcome largely relies on the operators’ experience. In such a case, a ML model can be ‘taught’ that exact sequence under varying process and environmental conditions. Various dynamic simulations can be created to show the different types of upsets that can trigger a shutdown, and the shutdown sequence can be included. The ML model, once tested against multiple simulations, can now be added as a closed-loop system and allowed to ‘operate’ the shutdown or start-up of the column to avoid damage to the unit or unwanted chemical releases. However, for more complex systems, AI still needs to evolve as a technology. Several refiners we have worked with have started on the path of AI implementation but have stopped short of full ‘autonomous plant’ systems. We have heard that the complexity of the processes at the refinery and the constant variation of feedstock and pricing have made it difficult to gain value from full AI implementation. A Lisa Krumpholz, CSO, Navigance GmbH, Lisa. Krumpholz@clariant.com The major challenges of optimising plant-wide operations
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PTQ Q1 2024
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