Process optimiser for large-scale applications
Yokogawa Electric Corporation has released Dynamic Real Time Optimizer, a solution in the company’s OpreX Asset Operations and Optimization range. Using a combination of first-principles simulation and multivariable predictive control technology, the Dynamic Real Time Optimizer software is designed to help optimise plant operations.
The integrated solution is based on Yokogawa’s advanced process control technologies and industry knowledge and simulation technologies developed by KBC Advanced Technologies Limited (KBC), a UK-based Yokogawa subsidiary. The solution enables plants to quickly respond to dynamically changing oil market and product market conditions and improve both productivity and profitability.
Advanced process control systems improve product yield and reduce energy consumption by maintaining temperature, flow rate, pressure and other process variables within a set range and keeping them as close as possible to their optimal values, even for production processes whose parameters are not easily controlled by conventional control systems. Such advanced control systems are increasingly used in facilities such as oil refineries, petrochemical plants, chemical plants and natural gas liquefaction trains. Advanced control solutions are attracting considerable interest due to their high economic value.
Multivariable advanced control systems determine the optimal value for plant independent variables. The optimal values are calculated based on information such as feedstock quality and prices, product specifications and prices, and energy costs. In a dynamically changing market, an advanced control system optimises the operation of a plant in real time based on changes in raw material properties, product demand and operating conditions.
Yokogawa says that Dynamic Real Time Optimizer calculates the optimal values for plant independent variables with a high level of accuracy and was developed based on KBC’s Petro-SIM process simulation platform, which includes functions for online use. This solution acquires data through online connections to control systems and plant information management systems (PIMSs), and carries out case study simulations to generate rigorous models and calculate additional inferred properties. Based on these models and operating conditions, the optimal values are calculated by applying mathematical programming and implemented online by the advanced control system.
Multi-unit optimisation based on simulations performed using rigorous models
With large-scale applications, advanced control systems need to be able to quickly process large amounts of data. Advanced control and optimisation systems must have the processing speed needed to calculate the optimal values for such large applications. However, commercially available products today are only suitable for small to medium-size applications and can only optimise the control of individual process units, according to the company. Yokogawa says its Platform for Advanced Control and Estimation is capable of high-speed processing and can collectively control large-scale applications across processes. Using this suite as its operating platform, Dynamic Real Time Optimizer can achieve multi-unit optimisation and control. Also, as it makes use of KBC’s simulation technologies, the Dynamic Real Time Optimizer software can obtain optimal values with high accuracy even with reaction processes that demonstrate non-linear characteristics.
Integrated solution with improved maintainability
The performance of advanced control and optimisation systems tends to degrade over time due to changes in operating conditions, catalyst activity changes, and the ageing of facilities. Yokogawa says the Dynamic Real Time Optimizer is designed to maintain advanced control performance even as a plant changes, by using an online Petro-SIM model. The structure of this solution makes it easy to maintain advanced control and optimisation systems, thus enabling operators to achieve optimal performance with nearly no change in maintenance workload.
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