Synthesis of Optimal Batch Distillation Sequences

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1 Presented at the World Batch Forum North American Conference Woodcliff Lake, NJ April 7-10, S. Southgate Drive Chandler, Arizona Fax Synthesis of Optimal Batch Distillation Sequences Matthew J. Engel Senior Process Engineer Air Products and Chemicals Inc Hamilton Boulevard Allentown, PA United States fax Bradley H. Cook Craig S. Landis Steven J. Tedeschi Anthony J. Zehnder KEY WORDS Batch Distillation, Optimization, Synthesis, Design, Simulation, Sequencing, Modeling ABSTRACT After the original design is completed, optimum Batch Distillation Sequencing/Operation in a chemical plant is usually a matter of trial and error that evolves as operators gain experience with the system. Some predictive tools exist for simulating batch separation systems, and they have been used with success in designing new separation systems and in analyzing existing systems. However, these tools require the user to define all the operation steps for each cut. The number of case studies required to approach an optimum scenario is very large, and is almost impossible to accomplish. Optimal design and operation, however, is the ultimate goal. In this paper we propose how to truly optimize a batch separation sequence. Combining the flowsheeting and modeling features of a process simulator provides an advantageous way to analyze and optimize operating scenarios for optimum batch separation sequencing. This approach is demonstrated with the optimization/debottlenecking of several batch distillation scenarios to separate and recover a heavy product, a partially water-miscible solvent, and water from a batch reactor effluent. Simulations identified a 25% increase in capacity with a minimum capital investment. Copyright 2002 World Batch Forum. All rights reserved. Page 1

2 Introduction Process engineers are often tasked to find more capacity in existing plants. In batch plants, the engineer traditionally starts with a cycle time analysis. The engineer evaluates a particular unit operation to determine the time required for each step. For example, in a batch distillation unit operation, the steps can be: charging, heating, total reflux, cut 1, cut 2, etc., and finally pot discharge. Based on the results of the cycle time analysis, the engineer then focuses on the step or steps that take the longest because these steps intuitively have the most impact on rates. The engineer then generates ideas and options for improvement, quantifies and validates the benefits for each option, selects the best option, and implements the solution. This paper focuses on the engineering quantification and validation of ideas for debottlenecking a batch distillation sequence where we propose fundamental optimization modeling as the evaluation tool. Fundamental optimization modeling uses rigorous thermo-physical properties and a detailed batch distillation unit operations model within a process simulator that has multi-variable optimization (MVO) capability. The proposed approach saves engineering time, allows the engineer to evaluate more ideas, and increases accuracy and confidence in the quantification of benefits. This paper: 1) Describes the debottlenecking methodology 2) Describes different approaches for quantifying and validating benefits 3) Demonstrates the proposed debottlenecking approach through an actual process application. Debottlenecking Methodology Generally, there is a business premise to debottleneck an existing plant. This business premise sets the objectives or goals the process engineer must deliver. The business premise for higher rates in a batch plant may be to meet customer demand or to better utilize the assets with more products. Often the business premise defines the boundaries or constraints of the ideas. Some of these constraints may be project execution time or capital spending. With the goals defined, the process engineer must generate ideas and options to debottleneck the plant. The ideas come from process understanding, cycle time analysis, operator feedback or simulation analysis. These ideas could be a larger reboiler, larger condenser, larger reflux pump, etc. The process engineer then quantifies and validates the benefits of each idea to debottleneck the plant. The production rate from each idea must be quantified. These quantified benefits must also be validated to determine if it they are truly achievable. Validation often requires the process engineer to look at constraints outside of the particular unit operation in question. After the benefits of each idea are quantified and validated, they must be sorted to determine if the business goals will be met within the defined constraints. If the idea will debottleneck the plant to the extent needed, a rough capital estimate is needed. With the capital estimate, the process engineer determines if the idea will meet the business constraints. The ideas are then sorted by a cost and benefits analysis to select the best option. The final step is to execute the selected option. This paper focuses on approaches to quantify and validate the benefits of debottlenecking options. Three approaches are described: Non-sequenced iterative approach Copyright 2002 World Batch Forum. All rights reserved. Page 2

3 Sequenced iterative approach Sequenced multi-variable optimization approach For better understanding of the three approaches, they are described and compared in detail through an example of an actual process application. Process Description The current bottleneck in the process is the crude product and solvent recovery system. As shown in Figure 1, the system consists of the following equipment: A single batch distillation column, including condenser and reboiler Five storage tanks A reflux pump A pot bottoms pump Three charge pumps The single batch distillation column performs three different distillations: Crude Product Distillation Wet Solvent Distillation Very Wet Solvent Distillation The purpose of the distillations is threefold: To remove a partially miscible solvent and water from the crude product To recover dry solvent for recycle into the reactor system To minimize organics in the waste water The crude product has a maximum specification for solvent and water. The dry solvent has a maximum specification for the water that can be recycled to the reactor. The waste water has a maximum specification for organics. Each distillation has its own cycle time, and each distillation must be sequenced to fully utilize the single batch distillation column. Since the solvent and water are partially miscible, intermediate storage and recycle within the recovery system is required. Copyright 2002 World Batch Forum. All rights reserved. Page 3

4 Figure 1: Process Flow Diagram As seen in Figure 1, the reactor effluent is stored in the Reactor Crude Storage tank. The Crude Product Distillation requires a wet solvent cut, a dry solvent cut, and the pot discharge. The pot discharge is the crude product which is further refined in other equipment. The dry solvent is recycled to the reactor. When several Crude Product Distillations have been completed and the Wet Solvent Storage tank is full, a Wet Solvent Distillation is performed. The Wet Solvent Distillation requires a very wet solvent cut, a wet solvent cut, and a dry solvent cut. Similarly, when several Wet Solvent Distillations have been completed and the Very Wet Solvent Storage tank is full, a Very Wet Solvent Distillation is performed. The Very Wet Solvent Distillation requires a wet cut, and the pot discharge is waste water. Note that the three distillations shown in Figure 1 are performed in the same distillation column. The solvent forms a low boiling azeotrope with water. Water is removed from the solvent in a classic pressure swing distillation. At high pressure (Wet Solvent Distillation), the water is concentrated in the azeotrope and the solvent is recovered as a pure component. At low pressure (Very Wet Solvent Distillation), the azeotrope is redistilled and the water is recovered as a pure component. See Figure 2 for the XY diagram. Copyright 2002 World Batch Forum. All rights reserved. Page 4

5 1 0.9 Low Pressure High Pressure 0.8 Vapor Mole Fraction Solvent Liquid Mole Fraction Solvent Figure 2: Solvent/Water XY diagram The sequencing of the three distillations is determined by Wet and Very Wet Solvent Storage tank capacities. If one of the Very Wet or Wet Solvent Storage tanks is nearly full, the contents of that particular tank must be distilled to lower the tank level to allow room for the cuts from a Crude Product Distillation. Engineering Quantification and Validation Non-Sequenced Iterative Approach For a batch plant, the engineer typically evaluates the cycle time for a unit operation and focuses on the step that takes the longest. In this application, the engineer would focus on the Crude Product Distillation because most of the column s time is spent on this distillation. The engineer would simulate the Crude Product Distillation with a batch distillation model and define each step and each process variable within the constraints of the equipment. The process steps are: charging, heating, total reflux, wet solvent cut, dry solvent cut, and pot discharge. The process variables in each step are column pressure, reboiler duty, reflux ratio, etc. The constraints of the system are vessel maximum allowable working pressure, column hydraulic limits, reflux pump capacity, heat transfer area, etc. The engineer evaluates the generated ideas and options in the simulation and quantifies the benefits. This approach is iterative because the engineer must validate the benefits after making a change in the simulation. The validation step verifies that equipment constraints are not violated. Additional iterations are required if the process variables are to be optimized. With the optimization iterations, the engineer again validates that equipment constraints are not violated. However, the results are biased to which variables the engineer manipulates in the optimization process. Copyright 2002 World Batch Forum. All rights reserved. Page 5

6 Because most of the cycle time is spent removing the solvent and water from the heavy crude product, one option would be to upgrade the reflux/distillate system constraint. The column could be simulated with an increased distillate rate. Similarly, other ideas could be simulated such as increased reboiler capacity. The engineer must consider the other constraints such as pump capacities, heat transfer capacities, column pressure limits, and column hydraulic constraints. Based on the simulation results, the engineer estimates a rate increase to justify any equipment upgrades. This approach is adequate if there are no interactions, such as recycle, among the three distillations. In this application however, this approach oversimplifies the problem. Although, most of the column s time is spent on the Crude Product Distillation, it is only one of three distillations performed in the column. The constraints placed on the simulation are only related to the Crude Product Distillation and do not take into account other constraints such as Wet Solvent Storage tank capacity. Any benefits quantified from the Crude Product Distillation may not be fully realized because of these other constraints. Since this approach does not consider other constraints, the results are biased because only one distillation was considered. The engineer could attempt to eliminate some of the bias through iterations with the other distillations. The other constraints such as Very Wet Solvent Storage tank size could be considered. The engineer could construct similar models for the Wet Solvent Distillation and Very Wet Solvent Distillation. These additional models would have to be simulated separately, each with equipment constraints that need to be validated iteratively. There are three distillations, each with several operation steps, each operation step with several manipulated variables, and each with internal and external constraints. The number of iterations to correctly quantify and validate the benefits of an idea, much less find an optimum, would be very large. A true optimum may never be reached since the time requirement would be prohibitive. Engineering Quantification and Validation Sequenced Iterative Approach To avoid iterations among the three distillation models, the engineer can combine the models into a single process flowsheet. In the flowsheet simulation, the batch distillations are sequenced as shown in Figure 1. The sequenced iterative approach requires the simulator have a function to relate the three batch distillation cycle times to the same basis. For example, if the process executes four Crude Product Distillations for every one Wet Solvent Distillation, the flow into the Wet Solvent Storage tank from the Wet Solvent Distillation needs to be scaled by ¼ to keep the flows into the tank on the same basis as the Crude Product Distillation. It is necessary to keep the flows to the cut tanks on the same basis so that the distillations are properly sequenced, for example, eight Crude Product Distillations to two Wet Solvent Distillations to one Very Wet Solvent Distillation. Without the function to relate the batch distillation cycle times to the same basis, the sequence would be one Crude Product Distillation to one Wet Solvent Distillation to one Very Wet Solvent Distillation, and the rate could not be calculated accurately. With the flowsheet, the existing process is simulated to establish a baseline model with each step and each process variable defined within the constraints of the equipment. The baseline model is defined by the current operating pressures, reflux ratios, reboiler duties, etc., and includes all processing steps. The engineer can then quantify the benefits of ideas and options in a similar fashion to the non-sequenced approach. Copyright 2002 World Batch Forum. All rights reserved. Page 6

7 This approach is iterative because the engineer must verify that equipment constraints such as heat transfer capacity are not violated in each distillation. However, the sequenced approach makes it easier than the non-sequenced approach to validate other constraints such as Wet Solvent Storage capacity. Unlike the non-sequenced approach that required iterations among three models, the sequenced approach only requires iteration on one model to quantify benefits and validate feasibility. The sequenced model automatically accounts for cut tank capacities. Additional iterations are required if the process variables are to be optimized. With the optimization iterations, the engineer again validates that equipment constraints are not violated. This approach is biased toward what manipulated variables the engineer changes to reach an optimum, and a true optimum may not be found. Even though the sequenced approach eliminates some of the iterations included in the non-sequenced approach, the number of iterations required to quantify and validate benefits can be large. Optimization would require additional iterations and further validation. The time requirement is still considerable. Engineering Quantification and Validation Sequenced Multi-variable Optimization Approach If the simulator used for the sequenced iterative approach model has a multi-variable optimization (MVO) feature, quantification, validation, and optimization can be performed simultaneously. MVO performs all iterations internally to validate the feasibility and find the global optimum. No manual iterations are required to validate or optimize. For example, MVO can determine the optimum reflux ratio, column pressure, reboiler duty, etc. for each step in each distillation within the constraints of pump capacities, maximum allowable working pressure, vacuum capacity, heat transfer area, and product specification. Unlike the non-sequenced and sequenced approaches that require time consuming manual iterations for each process variable, the MVO approach can simultaneously manipulate all process variables without bias since all iterations are internal. Input to MVO includes the variables to be manipulated, the constraints, and the optimizer objective function. MVO automatically perturbs each manipulated variable to satisfy the constraints and determine the global optimum. Since MVO manipulates all variables simultaneously, the engineering time to quantify and validate the benefits of an idea is greatly reduced. Subsequently, MVO allows the engineer to evaluate more ideas and options in less time. Application Using Sequenced MVO Approach In following with the debottlenecking methodology, the business premise was developed, ideas and options were generated, benefits were quantified and validated, and the options were sorted. Business Premise The product demand growth rate is expected to exceed 5% CAGR (compound annual growth rate). However, the existing assets are already fully utilized. This means that a 5% expansion is required for next year, and the capacity needs to be 22% greater in 4 years to meet demand. The product has a high value, so it is desired to capture as much of the demand as possible. At the same time, it is necessary to minimize capital spending. A major capital expenditure cannot maintain the plant s high return on investment. Copyright 2002 World Batch Forum. All rights reserved. Page 7

8 Ideas and Options Generated In the process application example (see Figure 1), the following ideas were generated to debottleneck the crude product and solvent recovery system: Install vacuum capability on the distillation column to exploit the pressure swing separation Increase distillate pump capacity to decrease Crude Product Distillation cycle time Increase Wet Solvent Storage capacity for more flexibility in distillation sequencing Implement super-batch (semi-batch) feed scheme to fully utilize pot capacity during Crude Product Distillation Upgrade reboiler to fully utilize column hydraulic capacity Increase Very Wet Solvent Storage capacity for more flexibility in distillation sequencing Some of these ideas were combined as additional options. The benefits of these options were also quantified and validated. Engineering Quantification and Validation As the first step, a baseline process flowsheet simulation was developed that matched current manufacturing procedures and rates. The flowsheet simulations results were validated with plant data. This validation gave confidence in the flowsheet simulation as a predictive tool. Next, MVO was added to the flowsheet simulations to evaluate and optimize the generated ideas and options. Input to MVO includes the optimizer objective function, the constraints, and the variables to be manipulated. The objective function is to maximize crude product rate. The following constraints must be satisfied: Crude product, dry solvent and waste water specifications Column hydraulic limitations All storage tank capacities All pump capacities Reboiler heat transfer capacity Condenser heat transfer capacity Vessel maximum allowable working pressure The manipulated variables for all steps in each distillation include: Operating pressure Reflux ratio Distillate Rate Other manipulated variables include: Wet solvent cut mass Wet solvent cut composition Very wet solvent cut mass Very wet solvent cut composition Copyright 2002 World Batch Forum. All rights reserved. Page 8

9 Results The benefits of the following options are displayed in Figure 3 as the percent increase over base capacity: Install vacuum capability on the distillation column to exploit the pressure swing separation Increase distillate pump capacity to decrease Crude Product Distillation cycle time Increase Wet Solvent Storage capacity for more flexibility in distillation sequencing Super-batch Option #1 Implement super-batch feed scheme to fully utilize pot capacity during Crude Product Distillation with: o Increased Wet Solvent Storage capacity o Increased distillate pump capacity Super-batch Option #2 Implement super-batch feed scheme to fully utilize pot capacity during Crude Product Distillation with: o Increased Wet Solvent Storage capacity o Increased distillate pump capacity o Increased Very Wet Solvent Storage capacity Super-batch Option #3 Implement super-batch feed scheme to fully utilize pot capacity during Crude Product Distillation with: o Increased Wet Solvent Storage capacity o Increased distillate pump capacity o Increased Very Wet Solvent Storage capacity o Upgrade reboiler to fully utilize column hydraulic capacity % Capacity Increase YEAR GOAL 10 0 Vacuum System Distillate Pump Wet Solvent Storage Option #1 Option #2 Option #3 Figure 3: Option Benefits Calculated as Percent Increase over Base Copyright 2002 World Batch Forum. All rights reserved. Page 9

10 Figure 3 shows that the vacuum system would not even meet the 5% capacity increase needed for next year. Only Super-batch Options 1 3 would meet the capacity needs in four years. After the benefits of each option are quantified and validated, they are sorted to determine if the business goals are met within the defined constraints. The options are sorted by a cost and benefits chart in Figure 4. Even though Super-batch Option #3 has the highest capacity increase, Super-batch Option #1 has the highest ratio of benefit/cost due to the implementation cost of Option #3. Super-batch Option #1 meets the business goal of achieving 22% capacity increase. Capacity Benefit to Capital Ratio Vacuum System Distillate Pump Wet Solvent Storage Option #1 Option #2 Option #3 Conclusions Figure 4: Cost and Benefits analysis Fundamental optimization modeling is proposed as the evaluation tool to debottleneck a batch distillation sequence. Compared to other engineering quantification and validation approaches, the sequenced multi-variable optimization approach allows the engineer to evaluate more ideas and options in a shorter amount of time with increased accuracy and confidence in the quantification of benefits. By exploring more ideas and options, the engineer can identify the best option, which may not be intuitive. Copyright 2002 World Batch Forum. All rights reserved. Page 10

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