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- Advanced Control Unleashed: Plant Performance Management for Optimum Benefit
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Ethanol is was unable to find a feasible solution that satisfied all predominantly taken at the fusel draw, located near the middle constraints in one go. Starting with a relaxed overhead ethanol of the column. The product flow rate product is mainly specification of 50 ppm allowed the model to first converge to governed by the concentration of ethanol ppm in the product a relatively relaxed specification, allowing for the creation of a stream, where a strict level is enforced.
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Failing to meet the reference point. Subsequent convergence to lower ppm product ethanol specification would result in a substandard specifications uses this as a starting point. This method product with a lower commercial value. Once the model was converged to 7ppm, it was very This paper focuses specifically at the optimization of the robust in finding out new solutions. The output data from these steady state models were then compared with industrial data.
After the validation of the steady state results with industrial data, this model was used for the optimization process where an adjuster block was introduced to streamline the optimization process. The selector block was assigned the task of changing the reboiler duty to bring the product ethanol concentration down to the pre- specified limit of 7 ppm. This is shown as ADJ-1 in Figure 2. Figure 1: Distillation process III. Modelling To identify throughput optimizing operational changes that could be made, it was necessary to build a steady state model of the distillation column.
This steady state model was then used to test different operational changes and quantify the effect they would have on the throughput and internal column factors, such as flooding. Based on the results obtained from the steady state model, plant trials were conducted using a subset of operational changes tested with the simulations that were practical to carry out at the plant. In the absence of a steady state model, as developed in this instance, every single Figure 2: Distillation column model using HYSYS simulation operational change would need to be tested in an industrial software setting, which can result in significant production losses, as well as safety issues.
Moreover, the steady state model gives a better insight into how operational changes affect internal column parameters than carrying out a simple plant trial. The process simulation software package HYSYS Figure 2 was chosen to build the preliminary model to match the steady state data provided by industry. Steady State Model Validation The analysis of the output data against the benchmark steady state data from industry revealed that the process simulation It was decided to specify outlet flow rates, reboiler duty and program HYSYS was well suited to modeling the behavior of the pressure profile.
These variables resulted in a stable these refining columns. The fluid package was adjusted within acceptable limits to arrive at the observed component distribution across the three The simulated column also showed the expected non-liner streams.
The industrial data estimated the tray efficiencies to fluctuate V. Assuming all trays in the column have the same efficiency, the tray efficiencies were adjusted to get a The initial focus of the optimization work was to arrive at better match on the component distribution.
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A theoretical approach to optimization was also adopted, to find Table 1: Tray efficiency ways to increase throughput without sacrificing product quality. If a certain operational change reduced the required reboiler duty, it was considered a positive operational change, as reboiler duty is directly proportional to internal tower traffic.
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Thus, reduced reboiler duty reduced tower traffic, which enables a greater throughput. A large number of solutions were found at the end of this process. To proceed further with these solutions it was necessary to look into the practical and operational impact of these solutions.
Upon closer evaluation, it was apparent that most changes would not be practical. The handful of solutions left were then carefully scrutinized in all aspects. The model was used to accurately predict the expected throughput increase The percentage error associated with the butanol distribution from each operational change. Key personnel at industry were in the fusel draw is significant. However this discrepancy did also informed of the proposed changes and their inputs were not hinder the validation of this model for the following taken into consideration.
This process identified the concentration in both the fusel and bottoms draws are following key operational changes as practical and easily estimates only. Changing the condenser pressure. Changing the feed and fusel flow locations. A slight shift in this profile will result in significant C. Changing the location of the product draw. Changing condensor pressure as iso-butanol, this generalization may be incorrect. Thus, reducing the reboiler duty required to make the 7ppm The absolute error associated with all component splits can be specification.
Advanced Control Unleashed: Plant Performance Management for Optimum Benefit
However lowering the pressure at the top of the regarded as minor and does not affect the ability of the tower increases the flooding factor of the trays at the top simulator to carry out optimization work. It is also important significantly. The top is the most vulnerable part for flooding, to note that the data gathered from the industrial process thus reducing the pressure at the top of the column is not practical. Therefore, increasing the pressure It is recommended to carry out these changes in three phases.
It is predicted The only drawback of this method is the marginal increase in that there will be a minor gain in reboiler duty. In phase two the reboiler duty required to meet the ethanol specification. As stated previously lowering the top In phase three, the feed rate should be increased until the pressure increases the flooding factor, whilst increasing the product ethanol specification of 7 PPM is reached.
Increasing throughput at a given top pressure changes the flooding profile of the Some distillation facilities in industry cannot combine the column, but only has a marginal effect on the flooding factors fusel stream with bottoms stream, as these plants are not at the top of the column.
If so it is recommended to lower both the fusel stream and feed stream. Changing the location of the product draw kPa 0. The remaining three plates at the top of the 0. If the topping 0. Thus, the product draw could be diverted from the 0. The reflux flow is located three stages higher up the column. Thus, the 0. Figure 3: Flooding factor The change should be carried out in two phases.
In phase one, Based on data shown in Figure 3, the industry was advised to the reflux should be diverted to the product draw, this should operate the distillation columns at a high top pressure to result in a lower ethanol ppm specification in the product. In optimize for throughput, provided that the plant was not phase two the feed rate should be increased, until the product suffering from steam shortages. If the plant is suffering from steam shortages it was advised to operate the column at slightly lower pressures as this allows VI.
A decentralized control structure is implemented in respectively. Therefore, an energy balance has taken so that flooding is avoided in the second column. The main idea of this vapour leaving the feed stage. Loops for control of operation and variables paired. The numbering of the loops corresponds to that of Fig.
The lighter and heavy compounds mentioned previously cannot be disclosed for confidentiality reasons.
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However, some indications Loop Controlled variables Manipulated variables can be given about their relative volatility in relation with the key CL1 Concentration in bottom 1st column Feed flow ethanol and water that includes them. Time in minutes.
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Scenarios and control structures tested in this work. Loops 1 and 3 ensure in Fig. Scenario 2 is the base case study, where the four loops the quality requirements of the product and waste. Loop 2 is used are controlled by PID controllers, independently tuned. A MPC con- to tightly control the pressure in the reflux drum and loop 4 keeps troller is implemented in scenarios 3 and 4 but in a different way. In the loading of the second column out of the flooding conditions by scenario 3, the MPC controller only acts upon loops 1 and 3. Loops 2 manipulating the vapour flow in the column. As for the rest of the and 4 are independently controlled by PID controllers.
In scenario 4, loops, they stabilize the system and cannot be used to modify the the four loops are controlled by the MPC controller.