Another model of distillation is Divided wall column DWC is used to simulate in the purification the CA from CCO.. One of the main advantages of batch distillation in Figure 1.7 is the p
LITERATURE REVIEW
Overview of Cinnamaldehyde
Cinnamaldehyde (CA), with the chemical formula C6H5CH=CHCHO, is an organic compound primarily found in its natural trans (E) isomer form This compound is responsible for giving cinnamon its distinctive flavor and aroma, making it a key ingredient in culinary and aromatic applications Natural cinnamaldehyde appears as a pale yellow liquid, as depicted in Figure 1.1, and is widely utilized in its liquid form across various industries.
Cinnamaldehyde is a yellow, oily liquid that is more viscous than water and has a strong cinnamon scent While concentrated cinnamaldehyde can cause skin irritation and is toxic in large doses, current health agencies do not consider it a carcinogen or a long-term health hazard Most of the compound is metabolized and excreted in urine as cinnamic acid, an oxidized derivative of cinnamaldehyde.
Both aromatic and an aldehyde, cinnamaldehyde has a mono-substituted benzene ring
Conjugated double bonds in alkenes typically result in a planar molecular geometry, enabling effective delocalization of electrons Cinnamaldehyde is specifically classified as trans-cinnamaldehyde, as its terminal carbonyl group is positioned opposite the aromatic ring across the rigid double bond, contributing to its distinct structural configuration.
The Global Natural Cinnamic Aldehyde Market is projected to grow at a CAGR of 4.17% and reach US$ 1326.54 million by 2027, reflecting steady industry expansion An in-depth analysis highlights the COVID-19 pandemic’s impact on the market, including regional effects and revenue fluctuations for key players up to July 2020, along with short-term and long-term market outlooks Natural cinnamic aldehyde has significant applications across industries such as perfumes, food and beverages, and metal and mining, driving demand and market growth.
Figure 1.2 Global Cinnamic Aldehyde Market, by the application (%) [1]
Cinnamaldehyde demonstrates significant antimicrobial activity, effectively inhibiting over 50% of bacterial growth in the oral cavity, as confirmed by a study from the University of Illinois, Chicago It is particularly effective in preventing the proliferation of bacteria and pathogens on the tongue, making it a valuable natural agent for oral health.
A study published in The American Journal of Chinese Medicine reveals that cinnamaldehyde, derived from Cinnamomum cassia Blume, effectively inhibits the growth of both gram-positive and gram-negative bacteria, as well as fungi including yeasts, filamentous molds, and dermatophytes This research confirms that cinnamaldehyde possesses strong antibacterial and antifungal properties, making it a potent natural remedy for combating various microbial infections.
Cinnamic aldehyde has a good fragrance holding effect It is used as the raw material in perfumes to make the aroma of the main spices more fragrant
Adding edible Cinnamic aldehyde to soap essence enhances fragrances of hyacinth, Cape jasmine, jasmine, lily of the valley, and rose These floral and fruity scents are widely incorporated into soaps, laundry powders, and shampoos, making products more appealing and fragrant.
Cinnamic aldehyde is a versatile ingredient used in the production of fruit essences like apple and cherry flavors These natural and synthetic flavors are commonly incorporated into a variety of products, including candies, ice cream, beverages, chewing gum, cakes, and tobacco Its rich aroma enhances the overall sensory experience of these food and confectionery items, making it a popular choice in the flavoring industry.
Cinnamaldehyde is primarily used as a flavoring agent in foods and beverages, such as liquid refreshments, ice creams, chewing gums, and candies, to enhance aroma and taste Additionally, it is incorporated into perfumes to recreate fruity and captivating fragrances, making it a versatile ingredient in the flavor and fragrance industries.
A technique for synthesizing cinnamaldehyde was first published in 1884 Today, cinnamaldehyde is most economically obtained through the steam distillation of cinnamon bark oil While it can be prepared from related compounds like cinnamyl alcohol, the first synthesis from unrelated compounds involved the aldol condensation of benzaldehyde and acetaldehyde, marking a significant advancement in its production.
Cinnamaldehyde is most economically obtained through steam distillation of cinnamon bark oil, making it a primary method of laboratory synthesis It can also be synthesized from related compounds like cinnamyl alcohol, the alcohol derivative of cinnamaldehyde The first synthesis involving unrelated compounds was the aldol condensation of benzaldehyde and acetaldehyde, a process patented by Henry Richmond on November 7, 1950.
CA occurs widely, and closely related compounds give rise to lignin All such compounds are biosynthesized starting from phenylalanine, which undergoes conversion
The biosynthesis of cinnamaldehyde begins with the deamination of L-phenylalanine into cinnamic acid, catalyzed by phenylalanine ammonia lyase (PAL) through a non-oxidative process involving its MIO prosthetic group PAL produces trans-cinnamic acid, which is then converted to cinnamoyl-CoA by 4-coumarate–CoA ligase (4CL) via an ATP-dependent acid–thiol ligation in a two-step reaction Finally, cinnamoyl-CoA is reduced to cinnamaldehyde by cinnamoyl-CoA reductase (CCR) using NADPH as a cofactor.
Plants have developed compounds like cinnamaldehyde to defend against herbivores, fungi, and bacteria, showcasing their natural protective mechanisms Research indicates that cinnamaldehyde is an effective insecticide, particularly against mosquito larvae, and serves as a mosquito repellent for adults While there isn’t conclusive evidence of direct health benefits, some studies suggest cinnamaldehyde may help prevent Tau protein tangles, potentially contributing to Alzheimer’s disease prevention or treatment Additionally, it is being explored for its role in metabolic health and obesity, as it appears to interact beneficially within these systems.
5 adipose tissue Cinnamaldehyde can also kill certain bacteria outright, while it inhibits the growth of others and prevents them from forming a biofilm.
Overview of Cinnamon Cassia Oil
Cinnamon is a perennial woody plant that can grow up to 15 meters tall with a diameter of up to 40cm at chest height, as shown in Figure 1.4 It features simple, ovate leaves that are arranged alternatively or oppositely, with two prominent veins extending from the leaf base to the tip The plant produces green, year-round foliage, although natural pruning is limited, as depicted in Figure 1.3 All parts of cinnamon contain essential oils, particularly the bark, which can contain 3-4% essential oils by dry weight Cinnamon produces small white or yellowish flowers at the tips of its branches through self-inflorescences There are two types of cinnamon—Cassia and Ceylon—each with distinct oil properties, as detailed in Table 1.1, highlighting their significance in the spice and essential oil industries.
Figure 1.3 Crude cinnamon Cassia Oil
Figure 1.4 Cinnamon tree Table 1.1 Two types of cinnamon cassia oil
Bark Cassia Oil Leaf Cassia oil
Made from Tree bark Tree leaf
Aroma Strong, robust Mild scent and musky aroma
Color Clear yellow to deep reddish- brown
Lighter in color, often brownish-yellow
1.2.1 Origin and distribution in nature
1.2.1.1 Distribution of cinnamon cassia oil in the world
Cinnamon is naturally distributed and cultivated as a key commodity in countries like Sri Lanka, Madagascar, Indonesia, China, and Vietnam These regions provide the specific climate, soil, and topographic conditions essential for the healthy growth of cinnamon trees Outside their optimal ecological zones, cinnamon trees cannot thrive or develop properly, highlighting the importance of suitable environmental factors for successful cultivation.
Figure 1.5 Supply of cinnamon oil
Vietnam is the world's leading exporter of cinnamon, renowned for its high-quality Saigon cassia cinnamon essential oil, which boasts a richer flavor and aroma compared to Chinese cassia cinnamon Vietnamese cassia cinnamon oil has a golden or dark-brown hue, with a sweet, woodsy, and slightly peppery flavor that closely resembles the scent of Vietnamese cinnamon The primary component of Vietnamese cassia cinnamon oil is aldehyde cinnamic acid, constituting approximately 80–95% of its composition This distinctive scent and superior flavor profile are key advantages of Vietnamese cassia cinnamon essential oil over Chinese varieties, contributing to its higher market price.
1.2.1.2 Distribution of cinnamon in Vietnam
Natural cinnamon trees historically thrived in the humid tropical forests across our country, from North to South However, natural cinnamon has now vanished, replaced by domesticated cinnamon trees cultivated for commercial purposes Over the years, our country has established four main types of cinnamon, reflecting its rich heritage in cinnamon cultivation and utilization.
7 cinnamon growing regions, each with its own natural nuances in terms of ethnicity and benefits from cinnamon
The major cinnamon growing areas in Vietnam (by area) are in the following order:
- Cinnamon Yen Bai: Cinnamomum cassia
Yen Bai cinnamon area is the largest in the region, covering up to 60,000 hectares, primarily located in Van Yen, Van Chan, Van Ban, and Tran Yen districts of Yen Bai province This region is characterized by divided and rugged mountain forests situated in the east and southeast of the Hoang Lien Son range, at elevations of approximately 300 to 700 meters The climate features an average annual temperature of 22.7°C, over 2,000mm of annual rainfall, and humidity levels averaging 84% The soil in the area develops on sandstone and schist formations, characterized by thick, moist, humus-rich, and well-drained properties, making it ideal for cinnamon cultivation.
- Cinnamon Quang Nam, Quang Ngai: Cinnamomum cassia
Tra Mi District in Quang Nam Province and Tra Bong District in Quang Ngai Province are located east of the Truong Son mountain range, encompassing approximately 17,700 hectares of cinnamon plantations These regions are renowned for their high-quality cinnamon cultivation, benefiting from favorable altitude conditions of around [insert specific altitude], which enhance the aroma and flavor of the spice The cinnamon from Tra Mi and Tra Bong is highly valued both domestically and internationally, contributing significantly to the local economy and promoting sustainable agricultural practices.
400 - 500 m; average annual temperature 22 o C; average rainfall is 2300 mm/year; average humidity is 85% Soil developed with parent rocks, sandstone or sandstone with thick, moist, well-drained soil layer
- Que Thanh Hoa, Nghe An: Cinnamomum Loureirii
Que Phong, Quy Chau (Nghe An Province) and Thuong Xuan, Ngoc Lac (Thanh Hoa Province) districts form a contiguous region east of the Truong Son mountain range, covering approximately 12,200 hectares of cinnamon cultivation This area features an average elevation of 300-700 meters, with a temperate climate characterized by an average temperature of 23.1°C, annual rainfall exceeding 2000 mm, and humidity levels around 85% The region boasts diverse and abundant plant species, with Que Thanh and Que Quy recognized as the top sources of high-quality cinnamon known for its superior essential oil content.
- Quang Ninh cinnamon: Cinnamomum cassia
Hai Ninh, Ha Coi, Dam Hoa, Tien Yen, and Binh Lieu districts in Quang Ninh province are picturesque, hilly regions located in the Northeast arc extending toward the sea Covering approximately 6,800 hectares, these areas are renowned for their extensive cinnamon plantations The mountainous terrain naturally blocks wind flow, resulting in high annual rainfall of around 2,300 mm, which creates ideal conditions for cinnamon cultivation Situated at altitudes of 200-400 meters and with an average temperature of 23°C, these districts provide a favorable climate for thriving cinnamon agriculture.
1.2.2 Demand, production, benefits of using and value of cinnamon cassia oil 1.2.2.1 World demand
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Figure 1.6 Importers of cinnamon cassia oil
Figure 1.6 indecated that the importers of cinnamon cassia oil while Malaysia: In
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Italy experienced the highest growth in the cassia cinnamon essential oil market between 2019 and 2020, with a remarkable increase of 92.18% from $81 million to $156 million This significant surge signifies a potential milestone for Italy's cinnamon oil industry, indicating robust market expansion and rising consumer demand during this period.
1.2.2.2 Benefits of using Cinnamon Cassia Oil
Cinnamon Cassia Oil has many amazing benefits related to health, mostly because of its chemical components Some of the most popular advantages are:
Diarrhea is a common and uncomfortable condition that everyone has experienced at some point, causing significant frustration Fortunately, recent studies show that Cassia cinnamon essential oil may offer an effective natural remedy for alleviating diarrhea symptoms Incorporating this natural oil into your health routine could provide relief and support digestive health, making it a valuable addition to your wellness arsenal.
Cassia cinnamon oil enhances blood circulation, ensuring your body receives vital oxygen and minerals for optimal health Its benefits include a warming sensation, pain relief, and the reduction of inflammation that can lead to disease Additionally, cassia cinnamon oil stimulates urination, aiding the body's natural detoxification process and helping eliminate toxins.
Cinnamaldehyde purification technologies from Cinnamon Cassia Oil
Batch distillation is a widely used separation process in the pharmaceutical, biochemical, and chemical industries Zhong Chan-yong et al investigated a method for preparing cannabinol (CA) from cannabidiol (CBD) using vacuum and molecular distillation techniques Their study achieved a final product purity of 98.66% and an 84.68% yield, which increased to 99.5% purity and an 85.63% yield after molecular distillation, demonstrating the effectiveness of advanced distillation methods in obtaining high-quality products.
Batch distillation allows for the efficient separation of multicomponent mixtures into high-purity products within a single distillation column, offering significant operational advantages CA, or trans-cinnamaldehyde, is predominantly found in cinnamon leaves, bark, twigs, and stalks, composing over 75% of cassia oil High vacuum batch distillation can produce cinnamic aldehyde with purity exceeding 99% from cinnamomum cassia oil, but this process faces challenges such as lengthy separation times and high energy consumption Additionally, a significant amount of CA can be lost into light and middle cuts during the process To address these issues, optimizing the structure of the distillation column and adjusting the reflux ratio are essential strategies aimed at enhancing efficiency and reducing product losses, making distillation process optimization critical for improved performance.
11 often maximizing distillate amount, minimizing operating time, and maximizing the techno-economical profit
There currently are no studies on the purification of cinnamic acid (CA) from calcium citrate (CCO) through continuous distillation Continuous distillation is an ongoing separation process where a liquid mixture of multiple miscible components is continuously fed into a system This process isolates desired products by preferentially boiling the more volatile components, which have lower boiling points, resulting in efficient separation of the mixture.
Large-scale, continuous distillation is essential in the chemical process industries for efficiently refining large quantities of liquids It is widely used in petroleum refining, natural gas processing, and the separation of gas liquids such as hydrogen, oxygen, nitrogen, and helium This method is particularly effective in low-temperature processing applications, ensuring efficient and reliable separation of valuable chemical components.
Industrial distillation is typically performed in large, vertical cylindrical columns referred to as distillation columns or fractionators with diameters ranging from about
65 centimetres to 11 metres and heights ranging from about 6 metres to 60 metres or more [2]
Continuous distillation operates on the same principle as traditional distillation: heating a liquid mixture causes it to boil, producing vapor with a different composition than the original liquid This vapor, when separated and condensed back into a liquid, becomes enriched with lower boiling point components This process effectively separates and purifies mixtures, making continuous distillation essential in industries like petroleum refining and chemical production.
In a continuous distillation column, a heated mixture is introduced and begins to flow downward As the feed enters, components with lower boiling points vaporize and rise through the column, while the remaining liquid flows downward As the vapor ascends, it cools, causing some of it to condense back into liquid This process results in the separation of components, with the vapor enriched in the more volatile substances continuing upward and less volatile components descending again, facilitating effective separation.
A feed stream is separated into two fractions: an overhead distillate product and a bottoms product The lightest components, having the lowest boiling points or highest volatility, exit from the top of the distillation column, while the heaviest components, with the highest boiling points, exit from the bottom The overhead vapor can be cooled and condensed using water-cooled or air-cooled condensers to recover liquid distillates The bottoms are typically heated by a reboiler, which can be steam- or hot oil-heated, or powered by a gas or oil-fired furnace, to facilitate continuous separation.
In continuous distillation, the system operates in a steady or near-steady state where key process variables such as feed rate, output streams, heating and cooling rates, reflux ratio, temperatures, pressures, and compositions remain constant over time Maintaining this steady state ensures consistent product quality and production rates, with minimal monitoring required The primary advantage of continuous distillation is its ability to sustain stable operation under constant feed conditions, and modern control systems can effectively restore steady state after process disturbances, ensuring efficient and reliable operation.
A continuous distillation unit operates by constantly feeding a feed mixture into the column, unlike batch distillation which requires a large vessel or reservoir for batch filling This design allows for uninterrupted separation processes, as the mixture is directly fed into the column where separation occurs The feed point’s height within the column can be adjusted based on specific process requirements to optimize separation efficiency and performance.
The design of a distillation column is influenced by the feed's composition and thermal conditions, as well as the desired product purity For simple binary distillation processes, methods such as the McCabe-Thiele technique and the Fenske equation are commonly employed to optimize design and operational parameters.
To design a distillation column for multi-component feed separation into multiple product distillates, computerized simulation models are essential These models facilitate accurate column design and enable efficient online operation, ensuring optimal separation performance across various feed compositions and product requirements.
Computerized simulation models are essential tools for evaluating the feasibility of modifying existing distillation columns to handle different feed compositions These models help optimize column operation by assessing adjustments needed when feed materials vary from the original design specifications, ensuring efficient and cost-effective processing.
Continuous distillation is vital for many industrial processes, often running 24/7 for 2-5 years between scheduled maintenance shutdowns While advanced computer control systems can manage the operational control of distillation columns, highly experienced personnel are essential to oversee real-time operations and perform routine daily maintenance to ensure optimal performance and safety.
Currently, there is no report for purifying CA form CCO; however, a divided wall column represents an effective method for process intensification It can reduce both capital and energy costs by 10-30% compared to traditional distillation columns In a three-component mixture, the divided wall column efficiently separates the light component into the distillate, the intermediate into the side product, and the heavy component into the bottom product Despite its advantages, designing a divided wall column is more complex than a traditional column due to additional variables such as reflux ratio, number of theoretical stages, liquid split, and vapor-liquid distribution, which require careful optimization.
The oil crises of the 1970s and 1980s highlighted the significant impact of rising energy costs on operational expenses, prompting the need to reduce energy consumption in distillation processes Consequently, improving energy efficiency has become a primary focus in new distillation system designs Advanced methods such as thermally coupled distillation columns (Petlyuk columns), heat-integrated distillation columns (HIDic), and divided wall columns (DWC) are widely used to enhance sustainability and reduce energy demand in industrial distillation.
Conclusion chapter 1
Cinnamaldehyde (CA) is a valuable compound with diverse applications, naturally occurring in the bark of cinnamon trees and other Cinnamomum species It is a pale yellow, viscous liquid where approximately 90% of its essential oil is composed of cinnamaldehyde This compound is primarily used as a flavoring agent in products such as chewing gum, ice cream, candies, and beverages, with concentrations ranging from 9 to 4900 ppm Additionally, cinnamaldehyde is utilized in natural perfumes for its sweet or fruity scents and serves as an effective fungicide It also functions as an antimicrobial agent, making it versatile in various industries Importantly, CA with 99% purity is a crucial material for synthesizing benzaldehyde, and purifying natural cinnamaldehyde from Cinnamomum species remains an efficient method for obtaining high-purity CA.
CA plays an important role to obtain CA99% with highest recovery ratio
❖ Advantades and disadvantages of batch column:
• Separating multi-component mixture by a single column
• A large amount of CA is loss
This thesis addresses the challenge of achieving a 99% recovery rate of the CA99% component while optimizing the heat duty at the bottom of the column Ensuring efficient recovery without excessive energy consumption is a key focus, emphasizing the importance of process optimization in chemical separation The research aims to identify effective strategies to improve recovery efficiency and enhance overall operational performance.
- Determine the equilibrium balance phase model for CCO system
- Determine the preliminary configuration of continuous distillation column and divided wall distillation column to recover CA99% with recovery ratio is 99%
- Simulation of the process of purifying CA99% from CCO by continuous distillation column system and divided wall column
- Integration energy to reduce the amount of heat duties of the bottom
METHOD OF STUDY
Determination of the thermodynamic model
Vapor-liquid equilibrium (VLE) data are essential physicochemical properties for the design, modeling, simulation, and optimization of CA purification processes To identify the most accurate thermodynamic model for distillation simulation, VLE data obtained from different models were compared with experimental results Currently, experimental VLE data for BA and CA are available only from Hong Li et al.'s previous study, which was used as a benchmark for model validation This research employed four thermodynamic models—NRTL, Wilson, UNIFAC, and UNIQUAC—to calculate the VLE data of the BA-CA binary system at pressures of 10 kPa, 20 kPa, and 30 kPa using Aspen Plus V10, facilitating a comprehensive comparison between theoretical predictions and experimental data.
When building a simulation, selecting the appropriate property estimation method is crucial, as it significantly impacts the accuracy of pure component and mixture property calculations In Aspen Plus, these methods are organized within the 'Property Method,' which includes estimation techniques for thermodynamic properties such as fugacity, enthalpy, entropy, Gibbs free energy, and volume, as well as transport properties like viscosity, thermal conductivity, diffusion coefficient, and surface tension Additionally, Aspen Plus offers a comprehensive database of interaction parameters that, combined with mixing rules, enable precise estimation of mixture properties, ensuring reliable simulation results.
Simulation method
2.2.1 Methodology of the continuous distillation column
Aspen Plus offers multiple unit operation options for solving distillation problems, tailored to the complexity of the application The DSTWU and RADFRAC models, as shown in Aspen Plus V10, facilitate preliminary column configuration and detailed design The DSTWU unit operation is specifically designed for single feed, two-product distillation processes, utilizing the Gilliland and Underwood method to calculate stages and reflux ratios This approach assumes constant molar overflow and relative volatility, enabling accurate estimation of the minimum number of stages and reflux ratio based on desired product recoveries During calculations, Aspen Plus also determines the optimal feed stage location and estimates condenser and reboiler duties, ensuring efficient distillation column design.
21 calculations are complete, Aspen Plus can produce tables and plots for the reflux ratio and stage profile a) b)
Figure 2.1 (a) DSTWU and (b) RADFRAC models in Aspen plus V10
RadFrac is a sophisticated distillation unit that requires more rigorous calculations than DSTWU, capable of simulating complex processes such as absorption, stripping, extractive distillation, and azeotropic distillation for solids, liquids, and gases It is particularly useful for modeling highly nonideal liquid solutions and processes involving ongoing chemical reactions The RadFrac column can handle multiple feed and product streams, including pump-around streams, and can simulate various packing types such as trays, random packing, or structured packing Due to its complex features and detailed capabilities, RadFrac is a more advanced simulation tool, extensively covered throughout this chapter.
2.2.2 Methodology of Divided wall column
The divided wall column (DWC) procedure methodology enables quick determination of key operational parameters such as the minimum vapor flow rate, minimum reflux ratio, and optimal number of stages for each section by selecting an operating reflux ratio, liquid and vapor split ratios, and the ideal position and configuration of the dividing wall Additionally, the compositions of the interconnecting streams between the prefractionator and the main column are estimated and established as initial conditions for simulation in Aspen Plus software This approach ensures efficient column design and optimized separation performance.
For the efficient separation of multi-component mixtures, a sequence of distillation columns is typically employed, often involving at least two columns arranged either in direct or indirect sequence to separate three-component mixtures An energy-efficient configuration, such as the Petlyuk setup, connects vapor and liquid streams between the pre-fractionator and the main column, optimizing separation performance In this system, the pre-fractionator first separates the mixture containing components A, B, and C (ranked by volatility), achieving a sharp separation between the light component A and the heavy component C, with the middle component B naturally distributing between the top and bottom of the pre-fractionator This setup enhances energy efficiency and improves separation accuracy in multi-component distillation processes.
22 obtain high-purity individual components at the top, middle and bottom of the main column
Designing, modeling, and simulating Double-Column Configurations (DWC) remains challenging due to increased design complexity Although Aspen Plus’s MultiFrac unit can model Petlyuk configurations, dedicated simulation modules for DWC are not available in leading commercial process simulators like Aspen Plus, HYSYS, or ChemCAD Additionally, the Petlyuk model in Aspen Plus offers limited flexibility for customization because of its closed software environment.
This article presents a comprehensive procedure for designing divided wall columns using the standard shortcut method (FUGK method) and the component net flow model The approach determines critical parameters such as reflux ratio, number of stages for each section, and liquid and vapor split ratios, ensuring optimal column performance Key assumptions underlying the method include simplified models of divided wall columns to facilitate accurate calculations This systematic design procedure enhances efficiency in the operation and structural configuration of divided wall column systems.
1) The relative volatility of components is constant
2) The vapor and liquid flows in each section of the divided wall column
3) The pressure of the system is constant
4) The heat transfer across the dividing wall is neglected
5) The heat losses from the column wall are negligible
6) Vapor – liquid equilibrium is achieved on each stage
7) The heavy non-key component is assumed to go down completely the bottom of the column, and the light non-key component is assumed to go up completely to the top of the column
According to Henry Z Kister (1992), key components in a feed mixture are the two substances whose separation is specifically defined, known as the light key (more volatile) and heavy key (less volatile) Non-key components include those that are lighter than the light key, referred to as light non-key components, and those heavier than the heavy key, called heavy non-key components Components that fall between the light and heavy key are termed distributed key components, playing a crucial role in separation processes such as distillation.
The procedure can be applied not only for ternary mixtures but also for multicomponent mixtures To simplify, we consider separation of a ternary mixture
A, B, and C, in which A is the lightest component and C is the heaviest component The feed flowrate is F(kg/h), feed composition zA, zB, and zC, and recoveries or purities of component in divided wall column are known
In multicomponent separation processes, the volatility of each component, represented by K-values, is assumed to be constant and ordered as KA > KB > KC The relative volatility, a key measure of separation ease, is defined as the ratio of the K-values of the more volatile to the less volatile component, ensuring that it is always greater than or equal to one Specifically, the relative volatility between components i and j is expressed as αi,j = Ki / Kj, indicating the ease with which components can be separated based on their volatility differences.
The feed composition is listed in order of their relative volatility: αA > αB > αC = 1
The minimum number of stages at total reflux can be estimated using the Fenske equation, which assumes that all stages reach equilibrium This method requires a constant relative volatility (α) throughout the column, ensuring accurate assessment of the minimum stages needed for efficient separation.
To determine the minimum reflux ratio, Underwood's equations are employed based on the assumption of constant molar flow rate, which simplifies the analysis The minimum number of stages and reflux ratio in a distillation column can be linked to the actual number of stages and reflux requirements using the Gilliland correlation, facilitating practical design considerations Additionally, the feed stage location can be estimated accurately by applying the Kirkbride equation In the case of divided wall columns, under the assumptions made, the configuration shown in Figure 2.2 (a) is equivalent to fully thermally coupled distillation as depicted in Figure 2.2 (b), enabling the use of a prefractionator in place of section 1 and the main column for sections 2 and 3 Interconnection streams between the prefractionator and the main column are added to optimize separation efficiency, offering a streamlined and energy-efficient distillation process.
Figure 2.3 (a) Divided wall column; (b) Thermally coupled distillation
Figure 2.4 Simplified model design of divided wall column
Based on Figure 2.2(b) of thermally coupled distillation, the main column can be simplified into two traditional columns as shown in Figure 2.3, considering the total heat duty for column II and a total condenser for column III The prefractionator is modeled as a traditional column with a partial condenser and partial reboiler, where the interconnecting streams serve as feed flows with superheated vapor and sub-cooled liquid conditions, respectively Components A and C are identified as key components in column I, while component B is the distributed component, with the top of column I containing primarily component A, some B, and a small amount of C, and the bottom predominantly C, some B, and minimal A Column II's primary function is to separate components A and B, making A and B key components, with component C acting as a heavy non-key component that exits from the bottom of column II Column III then separates the remaining components, completing the distillation process efficiently.
B and C Therefore, B and C are key of component A leaves from the top of column III
2.2.2.2 Material balance for divided wall column
As the Figure 2.3, material balance equations for each components are followed: For the component A:
The feed stream's flow rate (F) and composition (z A, z B, z C) are known Given the twelve unknown variables and only six equations, solving the system requires specifying six parameters Shotudeh et al proposed that these parameters should include: x A,D 2, x C,D 2, x A,W 3, x C,W 3, x B,S, x A,S, and x C,S, to achieve a determinate solution.
2.2.2.3 Minimum vapor flowrate of divided wall column
Minimum vapor flow rate of column I
In the column I, the recovery of component i in the top and bottom product defined as: τ i,T = x i,D 1 D 1
We have recovery of component A in the top product of the column I: τ A,T = x A,𝐷 1 𝐷 1 z A F
Assuming a sharp split of the lightest component, A, at the top of the distillation column is unrealistic for practical design, as it requires an infinite number of stages Therefore, the recovery of component A at the top must be less than 100% The recovery of component A in the bottom of the column can be expressed as τA,B, which is related to the top recovery by τA,T = 1 − τA,B, indicating that the bottom recovery depends on the feed composition, \(x_A,W\), and other process parameters such as \(W_1\), \(z_A\), and the flow rate \(F\).
Secondly, similarly, based on the assumption, and because the assumption of a sharp split of component A in the top of the column II is not suitable Therefore, we have: x A,W 1 W 1 < x A,S S
The recovery of component A in column I should be chosen between:
In the same way, recovery of component C in the top of the column I also analyzed
Pinch technology
This methodology focuses on reducing process energy consumption by determining thermodynamically feasible energy targets, or minimum energy requirements, through optimization of heat recovery systems, energy supply methods, and process operating conditions Pinch analysis, also known as process, heat, or energy integration, is a key tool used when temperature differences (∆T) are equal to or greater than 10°C, as illustrated in Figure 2.8 This approach enables efficient energy management and sustainable process design.
The pinch is a critical temperature point that marks the division between two distinct ranges in a thermodynamic process It represents the closest approach between the hot and cold composite curves, known as the pinch point At this point, the hot stream pinch temperature and the cold stream pinch temperature are identified, playing a vital role in optimizing heat exchange systems Understanding the pinch concept is essential for improving energy efficiency in thermal process design.
A heat exchanger network designed using the pinch method ensures that no heat transfer occurs from a hot stream above the pinch point to a cold stream below the pinch This approach optimizes energy efficiency by preventing unnecessary heat exchange between streams with incompatible temperature profiles Implementing the pinch design method enhances process performance and minimizes energy costs while maintaining effective heat recovery within the system.
Figure 2.8 Schematic of pinch technology
In pinch process design, no heat transfer is permitted across the pinch point, which acts as a critical boundary The area above the pinch point functions as a heat sink, while the area below serves as a heat source, optimizing energy recovery The minimum temperature difference (∆Tmin) is used to determine the appropriate equipment size, with higher ∆Tmin values leading to increased equipment costs This principle is particularly significant in oil refineries, where efficient heat integration reduces operational expenses and enhances process efficiency.
∆Tmin is in the range of 20-30 o C and for chemical it is in the range of 10-20 o C
- Heat must not be transferred across the pinch
- There must be no external cooling above the pinch
- There must be no external heating below the pinch
Method to evaluate the product quality
2.4.1 Determine the refractive index of the products
The refractive index defines how much a light ray bends when passing through a material, according to Snell's law (n1sinθ1 = n2sinθ2), which relates the angles of incidence and refraction at the interface between two media Refractive indices influence not only the degree of light refraction but also the amount of light reflected at the interface, as described by Fresnel's equations, and determine critical aspects such as the critical angle for total internal reflection and Brewster's angle, essential for optimizing optical device performance.
Figure 2.9 Refraction of a light ray
❖ Prepare measuring tools and equipment:
- Clean the refractometer with special soft tissues
- Adjust the refractometer to the standard position using the adjustment parts of the machine
- Re-calibrate the refractometer with distilled water or standards Measure the extraction (refractive index) of essential oil samples:
To accurately measure the refractive index of the essential oil, use a pipette to place a single droplet onto the measuring surface of the refractometer, ensuring it is centered on the refracting glass Proper placement of the oil sample is crucial for precise readings and reliable results.
- Close the top of the refractor and lock it tightly
- Adjust the refracting mirror to the best position so that the light shines most clearly
Adjust the adjustment knobs to ensure that the two halves of the flask—one white and one black—are perfectly balanced and centered precisely at the intersection of the two diagonals on the measuring plane Proper alignment of the flask guarantees accurate measurements and optimal results in your experimental setup.
- Reading of the measured value is made up in the meter, which is the value of the readings Refraction need to measure at a temperature of 200 o C
Gas chromatography (GC) is a widely used analytical technique in chemistry for separating and analyzing vaporizable compounds without decomposition It is essential for testing substance purity and separating mixture components, making it invaluable in quality control and analytical research Additionally, in preparative chromatography, GC enables the isolation of pure compounds from complex mixtures, facilitating further experimentation and applications.
A gas chromatograph consists of a narrow tube called the column, through which vaporized samples are carried by a continuous flow of inert gas, enabling separation based on chemical and physical properties Components of the sample pass through the column at different rates due to their interactions with the stationary phase lining or filling within the column The column is usually housed inside a temperature-controlled oven to optimize separation efficiency As chemicals exit the column, they are detected and identified electronically, allowing precise analysis of the sample's composition.
In this thesis, some parameters of GC such as:
Detector: FID Capillary Column: ADB-5 (code 122-5062) 60m I.D 0,25m Film: 0,25 micrometer
- The carrier gas is hyrogen (40mL/min) and Nitrogen (400mL/min), pressure: 27 – 30 psi
- The injection volume is 1μL using a split ratio of 1:50
Figure 2.10 Diagram of a gas chromatography
RESULTS AND DISCUSSION
Material characteristics
This study focuses on simplifying the complex mixture of Cinnamon Cassia Oil, which contains numerous components with incomplete equilibrium phase data To facilitate accurate simulation, five main components and their respective mass fractions are assumed, with an initial concentration of approximately 85% for the cinnamic aldehyde (CA) component, as detailed in Table 3.1 [15].
Table 3.1 Five main components in CCO
Choosing the best thermodynamic model
This study utilizes VLE experimental data for Benzaldehyde (BA) and Cinnamaldehyde (CA) from Hong Li et al.’s previous research to evaluate various thermodynamic models Four models—NRTL, Wilson, UNIFAC, and UNIQUAC—were employed in Aspen Plus V10 to calculate the VLE data of the BA-CA binary mixture at pressures of 10 kPa, 20 kPa, and 30 kPa The accuracy of these models was assessed using the root-mean-square error (RMSE), which measures the average deviation between experimental and calculated data, providing insights into the predictive performance of each thermodynamic model.
Figure 3.1 x,y-T diagram for the BA-CA system at 10kPa
Table 3.2 Experimental VLE data and calculated results by the different models in
Aspen Plus for binary system of BA-CA at 10 kPa
Experiment data [16] Calculated results y CA T
NRTL Wilson UNIQUAC UNIFAC y CA T ( o C) y CA T ( o C) y CA T ( o C) y CA T ( o C)
Exp 10kPa Hong Li at al NRTL
Figure 3.2 x,y-T diagram for the BA-CA system at 20kPa
Table 3.3 Experimental VLE data and calculated results by the different models in
Aspen Plus for binary system of BA-CA at 20 kPa
NRTL Wilson UNIQUAC UNIQUAC y CA T ( o C) y CA T ( o C) y CA T ( o C) y CA T ( o C)
Exp 20kPa Hong Li at al NRTL
Figure 3.3 x,y-T diagram for the BA-CA system at 30k
Table 3.4 Experimental VLE data and calculated results by the different models in
Aspen Plus for the binary system of BA-CA at 30 kPa
NRTL Wilson UNIQUAC UNIFAC y CA T ( o C) y CA T ( o C) y CA T
For BA-CA binary, Figures 3.1-3.3 showed that the differences between vapor phase mole fraction and boiling temperature calculated by NRTL, Wilson, UNIQUAC,
Exp 30kPa Hong Li at al NRTL
The UNIFAC models and experimental data show minimal variation, indicating that all four thermodynamic models fit the system effectively However, based on RMSE values presented in Tables 3.2-3.4, the NRTL model demonstrates the best performance with the lowest RMSE scores, including RMSE(y) values of 0.00338, 0.00298, and 0.00144, and RMSE(T) values of 0.2693, 0.15067, and 0.15067 at pressures of 10 kPa, 20 kPa, and 30 kPa, respectively Consequently, the NRTL model is selected for process calculations and simulations in this study.
Continuous distillation
This study utilizes the Fenske-Underwood-Gilliland equations to estimate the initial column configurations using a shortcut method before detailed calculations Based on the boiling points of the five components listed in Table 3.1, two continuous distillation columns are employed to produce CA from CCO, as depicted in the BFD diagram in Figure 3.4 Feed enters each column as saturated liquid at a specified pressure with a flow rate assumed to be F kg/h; the reflux ratio (R) is set to 1.3 times the minimum reflux (Rmin) for each stage In the first column, BA is designated as the light key (LK) and CA as the heavy key (HK), while in the second column, CA (LK) and CH (HK) are used, maintaining a recovery rate of 99% for LK and 1% for HK across all cases The short-cut calculations for the first column are summarized in Table 3.5, with the assumption that pressure drop along the column is negligible.
Figure 3.4 Block flow diagram (BFD) of purification process
Step 1 Assume the initial value of C1
Step 2 The distillate composition can be estimated by Hengestebeck – Geddes equation
Step 3 Solve the Underwood equation to determine Rmin
Step 4 Given that C1 is equivalent to Nmin and at a constant reflux R, the number of stage N is specified, calculate the value of Rmin from Gilliland’s correlation
𝑁+1 Step 5: Value of Gc should go to zero within a tolerance when C1 is converged
If Gc is approximately zero, the solution is converged for this time step Otherwise, the calculation procedure will be repeated from step 2 to obtain new value of C1
The study investigates the correlation between two different pressures, P1 and P2, across three cases: Case 1 with P1 equal to P2, Case 2 where P1 is less than P2, and Case 3 where P1 is greater than P2 The primary goal of the optimization process is to minimize the process's heat duty, thereby enhancing efficiency and reducing energy consumption.
The temperature difference between two streams D2 and W1 (∆T) is considered for energy integration if the ∆T = TD2 -TW1 is greater than 10 o C according to the pinch technology [17]
This study investigates the pressure range of 10-100mmHg for two columns with equal pressure (P1 = P2) Initial shortcut calculations, summarized in Table 3.5, were used to determine preliminary configurations Subsequently, rigorous simulations using the RadFrac model assessed the heat duties (QB1 and QB2) of columns B1 and B2 at various pressures These simulations were based on operating specifications from Table 3.5, ensuring consistent production purity and recovery rates for BA and CA at the top of both columns across all cases.
Table 3.5 Short-cut calculation and rigorous simulation results in two columns at
Increasing pressures P1 and P2 lead to a rise in both QB1 and QB2, with QB1 increasing slightly from 82 cal/s to 135 cal/s due to the small feed amount of BA (1 kg/h) In contrast, QB2 shows a significant increase from 1298 cal/s to 1512 cal/s, attributed to the higher feed rate of CA (8.5 kg/h) As a result, the total heat duty, QB,total, also increases with pressure This is because higher pressure raises the boiling point of the mixture, which in turn increases heat duties QB1, QB2, and QB,total Additionally, when P1 equals P2, the temperature difference between TD2 and TW1 remains consistent, with TD2 always being lower than TW1.
47 very slight Hence, the vapor in the top of the second column is not used to heat the reboiler in the first column
Figure 3.5 Plot of the relationship between P 1 , P 2 and Q B1, Q B2 , Q B,total
QB1, cal/s QB2, cal/s QB,total, cal/s
In Case 2, where P1 is less than P2, Table 3.6 presents the configurations for two-column simulations Based on Figure 3.5, when P1 is 1 mmHg, QB1 reaches its minimum value, leading to fixing the first column at P1 = 1 mmHg for further analysis The focus then shifts to investigating the heat duty of the second column with P2 varying from 15 to 100 mmHg As shown in Figure 3.7, an increase in P2 results in higher values of QB2 and the total heat duty (QB, total), which rise from 1277 cal/s to higher levels accordingly.
1528 cal/s, this is because in case 1 Besides that, Figure 8 showed that when
P1mmHg (fixed) and P2 increase then ∆T also rise Among them, at P2mmHg,
QB2 is minimum value, which leads to QB,total also obtain the minimum value with
The total heat duty, QB,total, is calculated as the sum of QB1, QB2, and 127,759 cal/s Since the temperature difference between the D2 and W1 streams is less than 10°C (∆T = 8°C), the vapor at the top of the second column is not utilized for reboiler heating in the first column Additionally, when the pressure P2 is measured in mmHg, different operational considerations come into play, influencing the overall process efficiency.
A temperature difference (∆T) greater than 10°C is maintained to ensure effective heat transfer between the columns [17] The overhead vapor in the second column is utilized to heat the reboiler in the first column, as illustrated in Figure 3.8 With the reboiler heat duty (QB1) set to zero, the total heat duty of the system is solely due to the second column's reboiler, amounting to 1326 cal/s Therefore, the total heat duty (Q_B,total) equals 1326 cal/s, combining QB1 and QB2 for efficient energy management.
Table 3.6 Short-cut calculation and rigorous simulation results in two columns at
Figure 3.7 Graph of the relationship between P 2 -Q B2 and comparison T W1 -T D2
Figure 3.8 Plot of T W1 at P 1 mmHg and T D2 at P 2 -100mmHg
Figure 3.9 Flowsheet of energy integration for two columns with case
QB1, cal/s QB2, cal/sat
In Case 3, where P1 > P2, Table 3.7 presents the configurations of two columns used for rigorous simulations Similar to Case 1, an increase in pressure results in a higher heat duty for the columns Therefore, the first column is maintained at a fixed pressure of P1 = 10 mmHg, while the performance of the second column is investigated at various P2 values to analyze the effects of pressure differentials on heat duty and overall system efficiency.
Increasing P2 from 5mmHg to 9mmHg causes a slight rise in QB2 and QB,total, as illustrated in Figure 3.10, which explains this relationship in case 1 Additionally, Figure 3.11 demonstrates that when P1 exceeds P2, it indicates that TD2 remains consistently smaller than
TD1 Therefore, the vapor in the top of the second column is not used to heat the reboiler in the first column
Table 3.7 Short-cut calculation and rigorous simulation results in two columns at
Figure 3.10 Plot of the relationship between P 2 and Q B2
Figure 3.11 Plot of T W1 at P 1 mmHg and T D2 at P 2 =5-9mmHg
In Figure 3.12, comparing the four cases reveals that Case 1, Case 2, and Case 3 have the lowest values at specific pressures P1 mmHg and P2 mmHg when energy integration (EI) is applied Notably, QB,total achieves its minimum value under these conditions with energy integration, indicating that EI can significantly reduce the energy consumption in the distillation process.
Figure 3.12 Plot of the comparison of cases with the minimum Q B,total values in cases 1,2,3
Divided wall column
The structural and operational parameters are determined by shortcut method, they are used as initial parameters for the simulation in as Aspen plus as show in Figure 3.13
Firstly, thermodynamics is used to calculate relative volatilities of the components
The operating parameters of the DWC are calculated using Excel worksheets, including the number of stages, product flow rates, component recoveries, and internal liquid and vapor distributions in the prefractionator and main column.
Table 3.8 Specification for short-cut calculation by DWC
Finally, data from the shortcut results were inputted into the Aspen plus software to make the rigorous simulations
Figure 3.14 Shortcut results of DWC Figure 3.13 Modul for the short-cut calculation
3.4.2 Sensibility analysis of Divided wall column
This section outlines the process of determining the design parameters of a divided wall column using our approach The analysis evaluates the impact of structural parameters—including wall height, vertical wall position, and the number of stages per section—to identify optimal configurations Ensuring the purity specifications of key product components is maintained across all scenarios, highlighting the importance of precise parameter optimization for efficient column performance.
3.4.2.1 Effect of the vertical position and height of the wall
In this section how the energy consumption changes are investigated when the vertical position and height of the wall change
The vertical position of the wall is adjusted from the bottom to the top along the column while maintaining a constant height of 20 stages The number of stages remains unchanged, and the feed and side stream locations are consistent with the shortcut results The dividing wall is positioned at zero in Figure 3.14, aligning with the shortcut outcomes, and is situated between stages 2 and 22.
The vertical position of the dividing wall varies relative to its initial placement, with lower positions indicating a negative range and higher positions indicating a positive range Understanding these positional changes is essential for accurate assessment and adjustment In the negative range, the dividing wall shifts downward, while in the positive range, it moves upward Recognizing these movements can improve alignment and ensure optimal structural performance.
The heat duty of the divided wall column varies with the position of the wall, with an initial value of 1219 cal/s when located at the lower position As the wall is positioned higher or lower, the energy demand of the column increases, demonstrating the influence of wall placement on process efficiency When the dividing wall is positioned two stages lower, between stages 0 and 20, the reboiler heat duty reaches 1905 cal/s, indicating increased energy consumption Conversely, moving the dividing wall three stages higher, between stages 5 and 25, reduces the reboiler heat duty to 1862 cal/s, highlighting the impact of wall positioning on reboiler energy requirements.
The result shows that the vertical position of the dividing wall from the shortcut results requires less energy when the structure changes
Figure 3.15 Effect of the height and vertical position of the wall on the heat duty of heat duty
The analysis reveals that the heat duty of the reboiler varies with the height of the dividing wall in the divided wall column When the wall height decreases from 20 to 18 stages, energy consumption reduces, with the reboiler heat duty around 1298 cal/s Conversely, increasing the wall height to 22 stages raises the reboiler heat duty to approximately 1410 cal/s Throughout these adjustments, the feed and side product positions remain unchanged, indicating that optimizing the number of stages can effectively reduce energy consumption in the column.
Hence, the procedure for the design of divided wall columns gives structural parameters corresponding to the minimum energy demand of the column
3.4.2.2 Effect of the number of stages
This section analyzes how the heat duty of the reboiler varies when the number of stages in one section changes, while all other sections remain fixed with their initial parameters The study highlights the impact of stage adjustments on reboiler performance, providing insights into optimizing distillation column efficiency Understanding these effects is crucial for process optimization and energy saving in chemical engineering applications.
Figure 3.24 illustrates how the heat duty of the reboiler varies with the number of stages in each section As the number of stages decreases in the negative range, the heat duty correspondingly declines, while in the positive range, increasing the number of stages results in higher heat duty This relationship highlights the impact of stage number adjustments on reboiler performance and efficiency.
Vertical position of the dividing wallHeight of the dividing wall
Figure 3.16 Effect of number of stages on heat duty of reboiler
Figure 3.16 indicates that the reboiler's heat duty rises as the number of stages per section decreases This is because reducing the number of stages, while maintaining the target product purity, necessitates increasing the reflux ratio Consequently, an increase in the reflux ratio leads to a higher heat duty for the reboiler, highlighting the relationship between stage number, reflux ratio, and reboiler energy consumption.
The analysis in Figure 3.16 demonstrates that increasing the number of stages in sections 3, 5, and 6 significantly impacts the heat duty of the reboiler, indicating a strong correlation between these stages and reboiler performance Conversely, variations in the number of stages in sections 1, 2, and 4 do not substantially affect the heat duty, suggesting these sections have a limited influence on reboiler energy requirements Optimizing the number of stages in key sections is crucial for enhancing overall process efficiency and minimizing energy consumption.
Increasing the number of stages in each section leads to a slight decrease in the reboiler's heat duty, as illustrated in Figure 3.16 However, it is important to recognize that adding more stages results in higher capital costs for the system Balancing stage number and system economics is essential for optimizing process efficiency and investment.
Comparison of three distillation models: batch column, continuous column
Table 3.9 Comparison of three distillation models
Batch DWC column of C.H.Ha et al
Table 3.9 reveals that the batch process in the C.H Ha study exhibits a significantly higher QB/P (cal/kg) value compared to continuous distillation and DWC systems, with values of 1.2×10^6 cal/kg versus 484,138 cal/kg and 445,071 cal/kg This suggests that both the continuous distillation tower and DWC systems have strong potential for industrial-scale applications, offering energy-saving advantages in the cinnamon cassia oil purification process.
Determination of the HETP of the various random packing
In the chemical industry, distillation is a crucial process used to purify mixtures based on differences in boiling points Packed columns offer advantages such as low-pressure drop, high mass transfer efficiency, and increased capacity, making them ideal for vacuum fractionation applications Performance metrics like Height Equivalent to a Theoretical Plate (HETP) and Height of Transfer Unit (HTU) are commonly used to evaluate packed column performance Several empirical and semi-theoretical models, including those based on two-film and penetration theories, have been developed to estimate pressure drop and capacity Notably, Bravo et al introduced the BRF model for calculating HETP and HTU in structured packing, which assumes gas-side mass transfer resistance dominates liquid-side resistance They subsequently refined this with the SRP model, incorporating corrective factors like the Surface Enhancement Factor (FSE) and liquid hold-up correction (Ft) to better predict effective surface area Billet and Schultes' BS model, based on extensive experimentation with different liquid-gas systems and packings, investigates mass transfer in packed columns and employs the penetration hypothesis, assuming continuous contact area refreshment between phases and packing-specific constants dependent on structure and material.
Separation efficiency is influenced not only by the choice of packing material but also significantly by the geometry of the packing Different types of packing are fabricated using SUS-304 stainless steel mesh, with mesh sizes such as 50 and 80 mesh, which correspond to specific packing types optimized for enhanced performance.
The study investigates various packings, including M-50, M-80, S-80, and O-80, with their detailed technical parameters outlined in Table 3.9 These packings were manually cut and shaped to ensure precise configuration, with their average sizes demonstrated in Figure 3.17 During each experiment, the HETP (Height Equivalent to a Theoretical Plate) index was calculated to assess the packing performance.
Table 3.10 Technical data of packaging materials
Figure 3.17 Various geometric parameters of the packings
This experiment utilized a 250 mL round-bottom flask as the still, heated by a heating mantle designed for round flasks, with thermometers positioned at both the bottom and top of the distillation system to monitor temperature A water-cooled condenser ensured efficient cooling during distillation, while a distillate and reflux vial were implemented to control the reflux ratio The column was insulated with a glass wool jacket to maintain consistent temperature Concentrations of n-hexane and cyclohexane in the distillates were measured using an Abbe Mark III refractometer (Reichert, USA) Cinnamaldehyde, obtained from cinnamomum cassia oil (99.0% purity, purchased from Arenex Co Ltd Vietnam), and benzaldehyde (99.0% purity, also purchased from Arenex Co Ltd Vietnam), were analyzed in the top and bottom products using Gas Chromatography (GC) with a SHIMADZU GC system, ensuring precise composition determination.
2010 plus (FID detector) system The chemicals used were as follows: n-hexane 99%,
GHTECH, China (CAS 110-54-3) and Cyclohexane 99.7%, GHTECH, China (CAS 110-82-7)
Experiments were conducted using a laboratory batch distillation column constructed from a glass tube with a 40 mm inner diameter and a length of 550 mm The column was packed with shaped random packings to optimize separation efficiency A liquid distributor was positioned just above the packing layer to ensure even liquid distribution throughout the column The complete experimental setup is illustrated in Figure 3.18, highlighting the key components of the distillation process.
Figure 3.18 Experimental setup for HETP evaluation
1 Heating mantle for round flasks, 2 Still (250 mL round bottom flask), 3,5 Thermometer, 4 Column with shaped random packings, 6 Condenser, 7 Vacuum pump, 8 Valve, 9 Liquid separation can
The bottom mixture was heated to its boiling point using a heating mantle, causing the refrigerant to fully condense the vapor at the top of the column Thermometers measured the temperatures of the reboiler and the top of the column to ensure optimal operation The efficiency of the packing was evaluated using standard mixtures, specifically measuring the HETP index with an n-Hexane/Cyclohexane mixture, due to their high relative volatility At atmospheric pressure (101 kPa), experiments involved preparing a 100 mL mixture of n-Hexane and Cyclohexane with a specific volume ratio, which was loaded into the reboiler and heated Once a stable liquid-vapor equilibrium was achieved, typically within about an hour, the distillation process was conducted efficiently.
In our study, 59 liquid samples were collected from both the top and bottom of the column and analyzed via refractometry to determine their compositions The system was considered at steady state after approximately 60 minutes following initial vapor release, confirmed by three successive samples with identical compositions These mole fractions at the column's extremities were used to calculate the number of theoretical stages (NTS), while the mass transfer process was evaluated using the height equivalent to a theoretical plate (HETP) Three different packing types were tested under various initial volume compositions of n-hexane and cyclohexane, specifically 30:70, 50:50, and 70:30, to assess their performance across different feed ratios, enhancing the understanding of separation efficiency under varied conditions.
A calibration curve was constructed to analyze the mixture of n-hexane and cyclohexane by relating their mole fractions to the refractive index, as shown in Figure 3.19 The samples were prepared through mixing and measured with a refractometer under ambient conditions The high correlation coefficient (R² = 0.998) indicates a strong agreement between the model and experimental data This calibration curve enables accurate interpolation of the distillate and bottom product compositions based on their refractive indices.
Figure 3.19 Calibration curve for mole fraction of n-hexane with RI
This article discusses the distillation of cyclohexane and n-hexane at 101 kPa under total reflux conditions Figure 3.20 demonstrates the relative volatility (α_avg) as a function of composition for this system, calculated using NRTL models in AspenPlus software to estimate constant relative volatilities These calculations are based on vapor and liquid mole fractions of n-hexane and cyclohexane The volatility of the components can be determined using the following equation, providing insights into their separation behavior during the distillation process.
Where: xi – liquid mole fraction and yi – vapor mole fraction; i = 1 to n; n – number of simulated data
Based on the simulated results, we can estimate the average relative volatility of the mixture:
avg – Relative volatility of n-hexane and cyclohexane;
K cyclohexane – Volatility of cyclohexane; n – number of simulated data
Figure 3.20 K-value for cyclohexane/n-hexane
Different packing types and volume fractions were tested in a laboratory distillation column to assess their impact on separation efficiency The top and bottom liquid compositions were accurately determined using calibration curves based on the mole fraction of the n-hexane/cyclohexane mixture and refractive index measurements The Fenske equation was employed to evaluate HETP values at specific column sections, revealing a relationship between mixture composition and HETP Results indicate that relative volatility and packing geometry significantly influence HETP values, leading to the assumption of a constant average HETP in process design and simulation Experimental data showed that HETP values remained relatively unchanged with increasing mesh size from type M-, suggesting mesh size has minimal effect on mass transfer efficiency.
50 to type M-80 The results can be explained that the opening (%) of two materials
Approximately 30% of packings are similar, but variations in geometric designs such as M, O, and S significantly influence HETP values due to differences in interfacial area and void fraction, which are critical parameters affecting mass transfer efficiency in structural packings As detailed in Table 3.10, the packing with O geometry achieved the lowest HETP value (HETPm = 0.045), indicating superior performance in mass transfer operations.