- Posted by Okechukwu Anosike on November 29, 2011
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Although modelling and simulation is traditionally the reserve of process design – used in the feasibility and FEED stages of an EPC project – it has been progressively adopted in operations activities. Most operational plants have a working model of their process, whether it is a refinery, chemical plant, an oil and gas platform, and so on. These models are being utilised in more sophisticated ways to increase productivity, profitability, efficiency, safety, operational flexibility and many other such reasons.
Historically, steady state simulations have been used to optimise various production processes. The objective functions of such optimisation are themselves functions of important operational inputs like feed composition and temperature, or of financial indicators like raw material costs or projected revenue. A good example of this application is in the crude refinery process where the ratio of a mix of crude types is determined as the ratio that generates the highest profit from product sales via optimisation. Such optimisations require accurate models before substantial value can be realised.
A refined implementation is the Real Time Optimisation (RTO). In addition to an accurate model, a high accuracy solver such as an equation-oriented solver is also prerequisite, along with a Distributed Control System (DCS) and a plant historian. The model is reconciled with the real plant to match the plant operations to within 1 – 2% offset. The offsets between plant variables and the model variables are determined, along with variable constraints, while plant parameters are also estimated. The plant historian provides all process conditions and inputs, and the model is optimised using the high accuracy solver. The result of the optimisation provides new operational points in terms of process conditions and inputs for the plant, which the DCS utilises to provide new set points for critical controllers in the process, while the historian logs measurement data. This process is repeated to determine new set points, with the frequency governed by how quickly the plant operations stabilises. This practice is proven to improve plant output and profitability.
Steady-state models are also used in endeavours such as energy management, where the objective is to reduce energy demand and consumption in plants, while also reducing the supply costs of the energy used. The least expensive ways to generate energy is determined, taking into account operational and system constraints, process unit interactions, electricity contracts and so on. Accurate and rigorous utilities demand for the process units are also modelled. With the supply side and demand side energy usage available on a single dashboard, operations are initiated to maximize use of most efficient process units, choose the best fuels and equipment drives, better adhere to contract terms and reduce penalties, reduce venting of steam, better cost accounting and so on.
For dynamic models, initial applications involved the investigation of transient operations issues like start-up and shut-down, however this was limited due to the complex mathematic operations required to solve time derivatives and other complex differentials. With the arrival of more powerful computers and solvers, dynamic simulation has become an integral part of FEED and has slowly moved into the operations.
A popular application is in Operator Training Simulators, where a dynamic model of the plant is used to train new plant operators. The trainee is set in front of a life control panel identical to that of the plant, and is forced to intervene in the plant operation by handling pre-programmed operational challenges. Instead of the plant bearing the brunt of the training exercises, the actions the trainee takes to stabilise operations are inputs on a dynamic model, which is under the hood of the training simulator representing the real plant. In this way, new operators are able to get up to speed in their duties within a relatively short period, with minimal risk to plant operations due to the experience they gain on the simulators.
Another application of dynamic models is in Advanced Process Control, specifically Model Predictive Control (MPC), using a dynamic model, a DCS and plant historian. The model, which runs at speeds up to a hundred times the real time due to powerful processors, must be robust enough to handle every conceivable operational point of the plant. The process conditions and inputs are read from the historian into the model, which then predicts the plant behaviour before such behaviour is observed in real time. The plant controllers via the DCS respond to correct for and handle process disturbances that have been predicted in the model before they occur in the plant to maintain stable plant operations. This control system is popular in unmanned oil and gas platforms and other remote or automated operations.
Modelling and simulation has come a long way over the past decades. These are only a few examples of the innovative applications in plant operations to improve the performance of process plants. As process industries continue to automate their plant operations, one thing that we can expect is that more novel applications for modelling and simulation would be introduced to tackle operational challenges.
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Category: Chemical Engineering, General Engineering, Plant Scale Up's, Process Engineering
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