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Home > Books > Solvents - Dilute, Dissolve, and Disperse - Insights on Green Solvents and Distillation

Evaluation of the Best Operating Conditions in Distillation Columns: A Case Study for the Separation between Nonylphenol and Dinonylphenol

Submitted: 13 October 2023 Reviewed: 13 October 2023 Published: 01 February 2024

DOI: 10.5772/intechopen.1003801

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Solvents - Dilute, Dissolve, and Disperse - Insights on Green Solvents and Distillation

Raffaello Papadakis and Vilmar Steffen

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Nonylphenol is a very important product for the chemical industry due to its widespread use as a base for several other chemical products. Among the major industrial sectors that use nonylphenol is the production of non-ionic surfactants, which are used from the home and personal care industry to the agrochemical industry. This study aims to define the optimal or quasi-optimal operating conditions for the separation system, which is composed of packed columns. Using simulation tools, the best operating conditions are attained, and the dinonylphenol subproduct generation is minimized. The commercial simulator Aspen Plus ® was used for this study as the analysis tool for the mentioned objectives. The developed model is validated with technical data, measures are taken in a nonylphenol plant, and parameters are used in the same plant. From the obtained data from the plant, the best process performance is evaluated regarding cost-benefit analysis and safety concerns. The study shows a potential to reduce the subproduct production by 30% and the reboilers’ heat loads by 2%.

• process simulation
• Aspen Plus ® software
• nonylphenol
• distillation column
• packed column

Author Information

Julio cesar ribeiro nunes.

• Department of Chemical Engineering, Federal University of São Paulo (UNIFESP), Diadema, SP, Brazil

Ricardo de Freitas Fernandes Pontes

Fabio rodolfo miguel batista.

• Department of Chemical Engineering (DEQUI-EEL), Lorena Engineering School, University of Sao Paulo (USP), Lorena, SP, Brazil

Rafael M. Matricarde Falleiro *

*Address all correspondence to: [email protected]

1. Introduction

Nonylphenol is a very important reactant in the surfactant industry, and its main application is in ethoxylated non-ionic surfactants, which use about 80% of the produced nonylphenol [ 1 ]. Nonylphenol is produced by a reaction between phenol, nonene, and a basic catalyst. The various nonylphenol applications range from the home and personal care industry to the agrochemical industry.

In chemical terms, nonylphenol is an alkylphenol, hence a phenol derivative where one more hydrogen atom in the aromatic ring is replaced by alkyl radicals. The most important alkylphenols are composed of alkyl radicals that have 2–12 carbon atoms. Besides their use as non-ionic surfactants, alkylphenols are also used as phenolic resins, polymeric additives, and agrochemicals. Nonylphenol is produced from an alkene (nonene) by acid catalysis (Lewis acid or ion-exchange resin), ensuring the replacement of hydrogen by the alkyl radical in the aromatic ring [ 1 , 2 ]. The overall reaction is highly exothermic (−23.7 kcal/gmol) and reversible as presented in Figure 1 .

Nonylphenol synthesis overall reaction.

The overall reaction process synthesizes the isomers ortho-nonylphenol and para-nonylphenol, and the latter is the major product. Besides the main product, the process also produces dinonylphenol as a subproduct, and this occurs by the reaction between one nonylphenol molecule with one nonene molecule. Dinonylphenol is much less used in the chemical industry compared to nonylphenol. Hence, an optimal operating point must be defined to reduce the dinonylphenol production to the minimum possible value because if this subproduct is produced in relatively large quantities, then the plant profit is reduced.

A productive process understanding is required to achieve nonylphenol synthesis optimization. Such understanding is done by theoretical studies that allow technical analysis and plant operation planning. Simulation software is used to evaluate the process under several different conditions without interfering in the plant operation. Moreover, by using simulation software, the optimization studies can be better directed saving time and resources that would be spent if the studies were directed toward a non-optimal configuration.

1.1 Nonylphenol production process

Figure 2 presents the nonylphenol productive process used by a major chemical plant in southeastern Brazil. This particular process consists of the following three major unit operations: reaction, phenol recovery, and nonylphenol purification.

Process flowchart for the nonylphenol production process.

The phenol alkylation reaction occurs in two serial mixture atmospheric reactors and is filled with an acid ionic exchange resin bed. Reactor 1 (R-1) operates within an 80–130°C range, and Reactor 2 (R-2) operates within an 85–135°C range. The temperature increase is gradual and based on the accumulated production of 1°C for each 300 ton of produced nonylphenol [ 3 ]. The reaction feed stream contains a phenol excess. The major raw material cost comes from the nonene purchase; hence nonene is the limiting reactant. The feed stream contains trace amounts of water from the phenol and nonane. The overall formation selectivity is 96.3% nonylphenol, 2.7% dinonylphenol, and 1% heavier compounds [ 2 ].

The process is catalyzed by an acid ionic exchange resin that replaces a Lewis acid. The use of the resin catalyst reduces the raw material demand, yields less subproduct production, and allows better control of mixing parameters. On the other hand, using resin catalyst requires increased capital costs, and the catalyst activity decreases with time. Therefore, reaction control must account for the activity reduction to ensure efficient reactors’ operation.

1.2 Phenol recovery

As the name suggests, this stage aims for the recovery of the unreacted phenol. Distillation is the used separation method. A column (D-1) with structured packing (SULZER BX 50) separates phenol from the heavier compounds in the Reactor 2 (R-2) effluent. The column operating pressure ranges from 0.240 to 0.267 bar. The estimated maximum pressure drop from the bottom to the top is about 0.027 bar. Column D-1 temperature profile lies within the 135°C (top) to 256°C (bottom) range.

Column D-1 feed stream is composed of nonylphenol, dinonylphenol, phenol, nonene, nonane, and traces of water. Since nonane lowers the nonylphenol yield in the reaction process, Column D-1 operates with a partial condenser to separate nonane from the unreacted phenol. The vapor distillate stream is mostly composed of nonane, nonene, and water, while the liquid distillate stream is phenol-rich. The vapor distillate stream is condensed in the Nonane Condenser (E-2), and the outgoing condensed stream goes to storage. The liquid distillate stream is recycled to Tank TK-4 at the beginning of the process, so the recovered phenol is reused in the reaction.

Column D-1 bottom stream is composed of heavier compounds, mainly nonylphenol, and dinonylphenol. As the bottom stream does not meet commercial purity requirements for nonylphenol product, the bottom stream goes to Column D-2 to separate nonylphenol from dinonylphenol.

1.3 Nonylphenol purification

The final stage aims, as previously mentioned, the attainment of nonylphenol products complying with market specifications (minimum 95% purity). The purification is done in Column D-2, which uses structured packing (SULZER BX 50). This column operates at a high vacuum (0.067–0.073 bar). Therefore, the pressure drop in the column must be limited to a maximum of 0.007 bar. Column D-2 operating temperature range is from 216°C (top) to 277°C (bottom).

Nonylphenol at market specification is produced at Column D-2 distillate stream. The purified nonylphenol is then cooled in Exchanger E-6 and stored in Tank TK-7. Therefore, the bottom stream is mainly composed of dinonylphenol, which is also cooled (Exchanger E-7) and stored (Tank TK-8).

2. Packed column and process simulation

In the 1950s, chemical industries used more frequently packed columns as distillation equipment [ 4 ]. Usually, packed columns are used for the separation of mixtures with high relative volatility difference, hence mixtures that are relatively easy to separate [ 5 ].

Packed column interiors are basically completely filled with a packing, which is either packed or random. Unlike a plate column, there is no space between stages, and the only empty spaces in the column are at the top, the bottom, and around the feed stream inlet(s).

Random packing is made up of small components such as Rasching and pall rings. These components have defined dimensions and geometric shapes. The column interior is filled with packing but in a random manner. Hence, the packing distribution varies randomly with the column height.

While random packing does not have a definite design, structured packing is designed to be the same throughout the stage height. Structured packing is usually formed by perforated metallic sheets that have corrugated sections, in order to create multiple parallel channels in the packing. The structured packing design forces the liquid and vapor streams to flow in countercurrent through the channels forming a liquid film in the packing surface. In this manner, contact between the liquid and vapor is maximized in the structured packing [ 6 ].

The main downside of structured packing is the need for a high feed stream flow rate, otherwise, the liquid film formation throughout the column transversal area is compromised. Consequently, low feed stream flow rates can reduce the interphase contact in the column resulting in low-efficiency stages [ 7 ].

Compared to structured packing, random packing has a lower capital cost and is installed more easily. However, structured packing is more efficient regarding distillation and imposes a lower head loss. Moreover, depending on the random packing component distribution, a preferential path can be formed, short-circuiting the column, and drastically reducing column efficiency [ 8 , 9 ].

In packed columns, other internals are essential to avoid a non-uniform distribution, a preferential path formation, or even liquid flowing through the column’s internal wall. Among these internals, the main ones are the distributors and re-distributors [ 8 ].

Considering the nonylphenol production process, packed columns are fundamental for safe and large-scale production as shown in Figure 1 . However, as many thorough locus studies, process optimization finds problems due to plant operation requirements. To perform any study test, plant production can be seriously reduced or even halted, yielding a profit loss. Additionally, relatively drastic alterations in process conditions can compromise the ability of the plant to return to its original operating conditions. Consequently, process simulators can provide tools to investigate different operating conditions, and these simulators are nowadays widely used in chemical industries.

Many process simulators are only able to evaluate a process under a steady state. However, some process simulators can predict the system behavior in a transient state, and this means that the simulator can predict the system behavior when a perturbation occurs. For plants that have different operating regimes, meaning that the process must migrate from one steady state condition to a different one, some transient state process simulators can perform real-time optimization. Such resources enable simulated studies a greater versatility, and a more rigorous evaluation and closer to the system’s actual response. This is crucial for the financial assessment of the productive process [ 10 ].

However, the results attained by a process simulator are only as good as the process model built by the engineers responsible for the process simulation. Therefore, the simulation engineers must have a thorough technical knowledge of the process. This means selecting an accurate thermodynamic model must be selected, the adequate unit operations involved in the process, and also arranging these unit operations to create a coherent productive process. Simulation engineers must also correctly interpret the simulation results, in order to ensure that the simulated model predicts the actual operation with precision.

Nowadays, several chemical process simulators are commercially available or even available for free. Aspen Plus® software from Aspen Tech is one of the main available process simulators. This software has an extensive data bank that includes component parameters and unit operations. Hence, this data bank allows Aspen Plus® to achieve accurate simulation results for many processes, specifically for distillation.

3. Case study: nonylphenol separation and purification

The case study focuses on the nonylphenol-production separation system. Therefore, the study encompasses both the phenol recovery-packed column (D-1) and the nonylphenol purification-packed column (D-2). The system was simulated using Aspen Plus® and is shown in Figure 2 .

For the study development, technical data was obtained from an actual nonylphenol production unit. The data contains information related to the equipment, process operating parameters, and stream composition data, including feed streams’ composition and flow rate. With this information, the flowsheet is built in the Aspen Plus® software. For all the different evaluated conditions, optimization is performed to minimize the dinonylphenol production.

3.1 Simulation development

The first step in building the simulation model involved obtaining actual stream data, specifically Column D-1 feed stream and Column D-2 distillate stream. Additionally, the operating parameters for both columns were investigated. A 3-year time interval for data gathering was established from March 2016 to March 2019. The historical data was supplied by the nonylphenol plant. From the data, the average and the standard deviation were calculated, in addition to assessing superior and inferior limits. Only steady-state operation data was considered, so plant startup and shutdown operation data were disregarded. Table 1 presents the maximum and minimum values for Column D-1 feed stream and Column D-2 distillate stream.

Specification limits for separation system sampling points.

The ideal stream composition is defined to attain the case study objective. This is done by evaluating the periods when dinonylphenol was minimum. The plant production data is available in monthly intervals; thus 3 months were selected as the minimum dinonylphenol-production periods.

3.2 Thermodynamic model

The definition of the selected thermodynamic model for the nonylphenol production system simulation is a crucial step for attaining accurate simulation results. The component’s thermodynamic, kinetic, and transport properties are evaluated using the adequate thermodynamic model [ 11 ]. In this manner, the component mixture thermodynamic relations and liquid-vapor equilibrium condition in the columns are precisely calculated, hence yielding accurate outgoing stream calculations.

Considering that the compounds present in the process are majorly very polar, that no electrolytes are present, and that the operating pressure is lower than 10 bar, the NRTL model was selected. Besides being the appropriate model in the decision tree given by the simulator, previous studies performed by the plant also used the NRTL model, and the results from these previous studies were deemed accurate.

3.3 Distillation column operating data validation

With the selection of the NRTL thermodynamic model, simulations were performed with Aspen Plus® using the columns’ operating date from the unit process flow diagrams (PFD) and equipment data sheet. As these documents also contain the unit material balance for the design condition, this means that the simulation results can be compared with the material balance results.

The packed columns are controlled by the bottom temperature as specified by design. Therefore, this variable was defined as a parameter that must be met by the simulation results. Another important validation point is compliance with the expected outgoing stream composition. In this manner, the nonylphenol in Column D-2 distillate stream must also be met by the simulation results.

For the simulation validation, two situations were considered: comparative evaluation with design conditions and with actual operating conditions obtained from plant instrumentation. For the design scenario, Column D-2 feed and distillate stream compositions are obtained by using the unit material balance, the packed columns’ top and bottom temperatures, the reboilers’ heat loads, and the hot utility flow rate used in these reboilers. For the actual operating scenario, the distillate stream composition is compared with the stream analytical composition measurements, and also by the actual column top and bottom temperature, as well as the measured column temperature profile.

3.4 Operating conditions evaluation

Two operating strategies are feasible to achieve dinonylphenol reduction, which is the study optimization objective. The first strategy consists in reducing the dinonylphenol in the reaction stage. The second strategy is for Column D-2 to produce a distillate stream (nonylphenol product) with the maximum allowable dinonylphenol content. This strategy can be interpreted as contaminating the nonylphenol product to the limit established by market specifications.

Using the 3-month low dinonylphenol yield, the average operating condition values were used as simulation input parameter values. In this manner, it is possible to compare the simulation results of the product outgoing streams for different flow rates. The results define the feed flow rate range that complies with the required production targets, and also the results show how dinonylphenol production varies according to the feed flow rate.

The simulation results also show how the reboilers’ heat loads vary with the feed flow rate. Hence, the hot utility (Thermex) consumption and the resulting operating costs are also evaluated.

3.5 Stream average properties and compositions

Tables 2 and 3 show the main values for the stream operating variables (input and output) for the nonylphenol purification stage. The input variables are used for the simulation model configuration, and the output variables are compared with the simulation results, so the results can be validated.

Average operating parameter values.

Average composition for D-1 feed stream and D-2 distillate stream (disregarding water content).

The average composition shown in Table 3 for Column D-1 feed stream was used as simulation input variables. Column D-2 distillate stream composition values are compared with the simulation results. The latter stream average flow rate is calculated for the result comparison. The table shows the production results for the period between 2017 and 2019. Notably, the nonylphenol product stream flow rate shows a decline tendency, since nonylphenol demand has also decreased. For the 3-year period, the average nonylphenol product flow rate is 1195 kg/h ( Table 4 ).

Nonylphenol average production.

2019 considers the production from January to March, and the projection for the remainder of the year.

3.6 Separation system validation

Table 5 data were obtained from the plant project data. Reflux ratio and reboiler heat input data were obtained from the unit design specifications.

Simulation input data for packed columns D-1 and D-2.

Although the actual Column D-1 condenser is a partial one, the simulation considered a total condenser for simplification purposes. The reason for the use of a partial condenser in column D-1 is for the nonane removal, which was not an objective of the study.

The used structured packing is Sulzer BX 50. The packing was inserted above and below the feed stage. The technical data used in the simulation was also obtained from the unit design data. For Column D-1, the upper part (rectification) packing has a 0.387 m diameter and encompasses Stages 2–4. This part of the column is 1.535 m in height and filled with packing. As for the lower part (stripping), the packing has 0.66 m diameter, and measures 1.345 m of height contemplating Stages 5–7. For Column D-2, the whole vessel has a 0.641 m de diameter. The rectification section is composed of Stages 2–3 and has a height of 0.68 m. The stripping section is composed of stages 4–5 and has a height of 1.195 m.

Thermex is the name of the hot utility used in reboilers. The fluid is the Dowtherm™. A saturated vapor at 280°C, as the design specified. From the fluid data sheet, the latent heat, and the overall heat transfer coefficient were obtained. The Thermex fluid parameter values are given in Table 6 .

Thermex (hot utility) properties.

According to design data, Thermex incoming and outgoing temperatures in the reboilers are equal, and this means that the Thermex vapor is saturated, and only latent is rejected from the hot utility.

Table 7 lists the input data for Column D-1 feed stream in the process simulation for the simulation model evaluation.

Feed stream input data according to design data.

For the first simulation scenario, Column D-1 and D-2 bottom temperatures were set to 256°C and 277°C, respectively. The reboilers’ heat loads were varied so the required separation specification was met. Though the heat load values were kept close to the plant design value. The validation results are given in Table 8 .

Comparison between simulation results and design condition.

temperature variations were calculated using the temperature values in Kelvin.

Overall, the simulator was able to reproduce with good accuracy the actual conditions, although some discrepancies can be observed. Column D-1 bottom presented a very good correlation between simulated and actual data for both major components (nonylphenol and dinonylphenol). However, the simulation yielded a relatively higher phenol content at the bottom compared to the actual data. This occurs as the phenol quantity in the bottom stream is small in absolute numbers both in the simulated results and the actual date. Therefore, small deviations can result in high relative variation. As for Column D-1, both reboiler (E-4) heat load and Thermex flow rate present also low deviation values, 3% and 5% respectively.

For Column D-2 distillate stream, only nonylphenol and nonene presented low relative variation values, and the reason is similar to the one described for Column D-1 bottom stream. An important difference is in the dinonylphenol subproduct stream (bottom), where the simulation yielded a value 68% higher than the actual value. This difference can lead to false optimal points.

For the evaluated production months, Column D-1 feed stream properties are given in Table 9 .

Column D-1 feed stream properties.

Table 10 shows the comparison between simulation results using the D-1 feed stream average flow rate and composition for 2019, and the evaluated 3-month data.

Comparison between simulation results and plant data.

For both the simulation results and the evaluated 3 months, Reactor R-2 effluent stream has a low dinonylphenol content. Hence, D-2 bottom stream (dinonylphenol subproduct) flow rate is small compared to the D-2 distillate stream (nonylphenol product) flow rate. In comparison to Table 3 data, Table 10 shows a significantly lower dinonylphenol content in the D-2 distillate stream. A possible explanation for this difference is that the plant usually operates Column D-2 with a low reflux ratio to deliberately produce a high dinonylphenol content product stream but still in compliance with market demands.

Column D-2 temperature profile is not altered significantly with the flow rate. This is expected as feed composition has a much greater effect on column temperature than feed flow rate. The results prove that as composition varies, temperature varies significantly. Another parameter that has a direct influence on column temperature is the column operating pressure.

As the nonylphenol product becomes purer, the stream’s flow rate decreases. The lower dinonylphenol content in R-2 effluent makes the separation in Column D-2 easier and that can be noted in the lower reflux ratio. Hence, this reduces the plant’s operating cost.

Since composition has a larger effect on column performance than feed flow rate, simulation results are obtained for different dinonylphenol to nonylphenol ratios (DNF). The DNF ratios for the 3 evaluated months are given in Table 11 .

DNF ratio variation for the 3 evaluated months.

In January 2017, the largest DNF ratio variation occurred as the dinonylphenol content in D-2 feed stream reaches the lowest value. This means that for such composition, the D-1 distillate stream already meets market requirements, and this stream could even completely by-pass Column D-2.

3.7 Energy consumption analysis

As columns’ operating conditions are altered, an assessment of the reboilers’ heat loads is made. These heat loads have a direct influence on the hot utility heat load, and also on the financial expenditure to operate the reboilers. Table 12 shows the heat load values per nonylphenol produced.

Simulation results for reboiler heat loads.

For Reboiler E-4, only in March 2019, the heat load surpassed the 2019 average values. For Reboiler E-5, the heat load is lower than the average 2019 value for all months. The same is repeated for the sum of both heat loads.

Therefore, by repeating January 2017 conditions, a 2% energy reduction is attained.

Thermex vapor is generated in a natural gas boiler. The following considerations are made: 100% efficiency in heat transfer from natural gas combustion to Thermex, the natural gas inferior calorific value of 33,500 kJ/m 3 , the natural gas density is 0.7902 kg/m 3 (IEA, 2019), and the natural gas cost is R$2.48/m 3 . Therefore, the heat load cost is 6.29.10 −5 R$/kJ. A financial assessment is made according to the mentioned values and Table 12 values. The results are given in Table 13 .

Expenditure variation for the heat loads’ sum in the 3 evaluated months.

Using the January 2017 value, the expenditure savings in a whole year could amount to R$ 9927.19.

3.8 Minimization of dinonylphenol production

Based on the months when dinonylphenol subproduct stream flow rate was minimal, the operating parameters of the nonylphenol production unit during these months are recommended as operating setpoints. As January 2017 presented the lowest dinonylphenol subproduct stream flow rate, the month’s parameters are given in Table 14 .

Recommended new operating setpoints.

3.9 Analysis of the operating pressure effect

As the purity of distillation column outgoing streams is defined according to process requirements, the distillation column design depends on the definition of two degrees of freedom: the column operating pressure and the reflux ratio (or the number of stages). Column operating pressure influences the equilibrium curve for the heavy and light key components, the column temperature, the condenser and reboiler heat loads, the condenser and reboiler exchange areas, the required utilities, the column diameter, and the column wall thickness, among other factors [ 12 ]. Therefore, defining the pressure is a crucial step in distillation column design.

The greatest challenge in defining the column operating pressure is that there is no established methodology for doing so, instead a handful of heuristics are often used and that can lead to sub-optimal designs [ 13 ]. Thorough optimization studies can be performed to attain a more cost-efficient design [ 14 ] but such studies are not always feasible as many economical parameters can be at the maximum, estimated with a reasonable degree of certainty. Even if the parameters were known, optimizing a distillation column is a time-consuming effort, and depending on the software and hardware used, the obtained solution is likely to be a local optimal and not a global optimal. That does not mean that operating pressure optimization should not be pursued but it means that engineers prefer to use heuristics to quickly attain a solution regardless of whether it is an optimal one or a feasible one.

In spite of optimization study difficulties, project and process engineers must comprehend the operating pressure effects on the distillation column operation. Firstly, relative volatility between light and heavy key components is usually inversely proportional to the operating pressure [ 15 ]. Hence, the lower the column operating pressure is, the higher the relative volatility is, and this means that at lower pressure, separation between key components can be made in fewer stages and with a lower reflux ratio. Evidently, how much pressure affects relative volatility depends on the components being separated. Using Column D-1 feed stream data from Table 7 , the minimum number of stages, minimum reflux ratio, and temperature profiles for Columns D-1 and D-2 are analyzed as a function of the column operating pressure. The results for pressure variation are given in Tables 15 and 16 . For Table 16 , the simulations are made for a D-1 operating pressure of 0.240 bar abs.

Column D-1 operating pressure variation effect.

Column D-2 operating pressure variation effect.

As nonylphenol and dinonylphenol have a considerable difference in molecular weight, and consequently in relative volatility, the results in Tables 15 and 16 show that pressure variation has a small effect on the minimum number of stages and minimum reflux ratio. This small effect is also due to the relatively narrow simulated pressure variation range. As for the columns’ bottom and top temperatures, despite the very low pressures, the temperatures are high. This is expected since dinonylphenol is a large molecule with a very high boiling point. As a suggestion for the plant’s future optimization efforts, operating Column D-1 with an even lower pressure could yield a bottom temperature where Thermex could be replaced with high-pressure steam. As for Column D-2, this is not possible, as this column already operates near full vacuum.

4. Conclusions

The study aimed at subproduct (dinonylphenol) reduction and also heat load reduction. For these aims, a thorough investigation of the design conditions, and historical plant data was made to determine the months that yielded the lowest dinonylphenol production. The plant relies on two strategies to reduce the dinonylphenol production, and these are defining reactor conditions that minimize conversion of nonylphenol into dinonylphenol and increasing dinonylphenol content in the product (nonylphenol) stream to the maximum allowable composition. Aspen Plus® was the simulator used to investigate a new operating condition for the nonylphenol production unit to attain the study goals.

The results indicate that a 36% subproduct reduction is possible. This value is achieved by a comparative analysis between simulation results and data from months where subproduct production reached minimum values. By simulating the nonylphenol plant design conditions, operating parameters are obtained for the reaction, phenol recovery, and nonylphenol purification stages. These recommended parameters are validated using historical plant data. The new simulated condition reduces the reboilers’ heat loads, and this can lead to a 2% reduction in hot utility consumption.

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