Essay: Research on online optimization RTO

Optimization of process operation is of considerable interest in industry due to increasing global competition and tightening product requirement. On-line optimization is an appealing concept because it is at the level of control hierarchy at which the business decision are integrated into the operation. Advances in the speed and power of computers at lower costs are making on-line optimization is more cost effective method of improving plant performance.
Following lists the benefits from steady state optimization as:
• Improved product yield and quality
• Reduced energy consumption and operating costs
• Increase the capacity of equipment, stream factors
• Less maintenance cost and better maintenance of instrumentation
• More efficient engineers and operators as a tool for process troubleshooting and operation
• Tighter, lower cost process design if new plant designs include in a real time optimizer
Method:
Real time optimization is a type of closed-loop process control that attempts to optimizes process performance (usually measured in terms of profit) on-line, in real-time. These closed-loop control systems are distinct from traditional process controllers, in that they are built upon large-scale, model based optimization systems. Specifically any RTO research efforts include,
• Continuous disturbance tracking
• RTO design for the plant described by differential-algebraic equations
• Convergence of closed-loop RTO system
There are several fundamentals gears for the smooth operating of an RTO solution. The RTO loop is an extension of feedback control process and consist of subsystems. The RTO system use rigorous process models and current economic information to predict the optimal process operating conditions. Additionally, RTO can mitigate and reject long term disturbances and performance loses ( due to fouling of heat exchangers or deactivation of catalysts).Finally the rigorous process model can be used for maintenance process, advanced process control, process design, facility planning and process monitoring.
Control layer and RTO concept:
Usually the process control is stratified into several layer, which have different response time and control objectives. RTO is located in an intermediate layer that provide the connection between plant scheduling (medium-term planning) and the control system (short-term process performance). In a plant control hierarchy, process disturbances are controlled using process controllers whereas the RTO system must track changes in the optimum operating conditions caused by low frequency process changes. A typical RTO system includes the following elements: model updaters, model-based optimizer, and result analysis and process control.
MPA method:
The RTO cycle starts with steady state detection module responsible to analyze the process measurement and to decide, based on statistical criteria if the plant has reached steady state. Then the stationary point goes through the data reconciliation and gross error detection stage. Further the screened information is used in the parameter estimation module to update the model parameters. Then the updated model is employed to find a new operating point that hopefully maximizes the plant profit. Finally this condition is passed to the process control layer as set points for the controlled variables.
ISOPE method:
One of the difficulties with the optimization is the mismatch between the model and real plant. The Integrated System Optimization and Parameter Estimation method was developed to handle the structural plant-model mismatches, complementing the measurements used in the MPA method with plant derivative information, to reduce the offset created by the structural mismatch. ISOPE still retains the parameter estimation step and economic optimization steps as in the MPA .However. ISOPE optimizes a modified economic function, adding a term coming from the parameter step that allows a first order correction
MA method
The idea behind Modifier Adaptation (MA) method is to use measurements to correct the cost and constraint predictions between successive RTO iterations in such a way that the KKT point for the model coincides with plant optimum. The fundamental difference between the MA and ISOPE framework are how the modifiers are calculated and the parameters updated. In MA,  the modifiers is calculated from the derivatives of economic objective function with to inputs(u),while the ISOPE method uses the derivatives of outputs(y)with respect to the inputs (u).Moreover, the parameters are updated during ISOPE iterations whereas MA uses a fixed parameter set during optimizations, i.e., there is no parameter updating. With this configuration, MA method also suffers from some problems as numerical optimization issues and lack of accurate plant derivative information.
SCFO method
The SCFO method initially proposed by Bunin, Francois and Bovin and modified for practical implementations by Bunin; Francois and Bovin adapts the non-linear optimization theory to RTO problems. The method is devised to calculate the plant optimum without violating any hard constraint and improving the plant profit at each RTO iteration, executing a projection problem based on information of plant derivative and topology. In other words giving a target, (a possible future RTO point predicted by any RTO algorithm, MPA for instance) SCFO method o=implements a correction to this   target, based on plant derivation information.
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2.3.4 Advantages and Disadvantages
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Advantages:
RTO’s give maximum consumption of the system and give us more output while using all resources and keeping all the devices active. There is little or no down time in these system.
There is very little time assigned to shifting task in these systems. For example in older system it takes about 10 micro seconds in shifting one task to another and in latest system it takes about 3 micro seconds.
Due to small size of programs RTOS can also be used in embedded systems like in transport and others.
RTOS is error free that mean it has no chance of error in performing tasks.
RTOS can be best used in any applications which run 24 hours and 7 days because it do less task shifting and give maximum output.
They are used in vast areas like digital appliances, home video games, wind power system, intelligent transport system and robots in industry.
Memory allocation is best managed in these type of system.
Disadvantages:
There are only limited task run at the same time and the concentration of these system are on few application to avoid errors and other task have to wait. Some time there is no time limit of how much the waiting task have to wait.
RTO used a lot of system resources which is not as good and is also expensive.
Multitasking is done few of times and this is the main disadvantage of the RTOS because these system runs few task and remain focused on them. So it is not best for the systems which use a lot of multi-threading because of poor thread priority.
RTOS uses complex algorithms to achieve a desired output and it is very difficult to write algorithm for a designer.
Beneficiaries:
Main applications of real time optimization can be stated as follows:
Elimination and modification of random errors
Dynamically describing performance deviation of key equipment from their set points
Automatically optimizing plants performance
Automatically performing fault detection(instrumentations malfunction, leak)
Intelligent computation of data which their measurements are unavailable(temperature, pressure, flows)
Calculating and reporting consumption of raw material and production of product at any time
Assessing energy consumption of the entire plant and the equipment at any desired time instant
Determining equipment’s performance loss over time(products, exchangers, columns)
Studying and monitoring history of the equipment and  monitoring
Sending computed data and results via computer networks to the desired locations in the plant