Distillation Column Research Articles

HAZOP ANALYSIS OF AN ATMOSPHERIC PETROLEUM DISTILLATION UNIT AND SIMILARITY IN SUSTAINABLE AVIATION FUELS (SAFs) PRODUCTION PROCESSES

The production of petroleum derivatives stands out worldwide among the non-renewable energy production processes, as the most well-known worldwide. In this study, an atmospheric distillation column that processed 100,000 barrels/day (662.5 m3/h) of crude oil was simulated using CHEMCAD® software. From this procedure, the Process Flow Diagram (PFD) and the Piping and Instrumentation Diagram (PI&D) for the unit, corresponding to its automation, were obtained. Subsequently, a HAZOP (Hazard and Operability Study) analysis was conducted in order to identify risks in the unit that, although not dangerous, could compromise its ability to achieve its productivity. The main risks of the process were related to fires, explosions and environmental contamination from leaks and ruptures in pipes, pumps, heat exchangers, the column itself, among other equipment. The HAZOP analysis was able, through the use of guide words, to identify possible operational risks arising from deviations in operating intentions, such as the possibility of fires, explosions, environmental contamination and their consequences. It was possible to identify the risks associated with the selection of materials, the mechanical design of the equipment and the specifications of the accessories. Therefore, the study carried out is an important survey for reducing possible failures in the oil industry. And, since this study is an in-depth study on the distillation processes for petroleum-derived products. It is understood that their in-depth study, as a result of the development of new renewable products, especially sustainable aviation fuels (SAFs), because they are similar purification processes, employ the same methodology to identify possible failures in production.

Hazard, risk, reliability assessment and improvement of Darlington Tritium Removal Facility (DTRF) cryogenic refrigeration system

Tritium is a radioactive isotope of hydrogen, produced in heavy water, during CANDU reactor operations by neutron absorption. The tritium removal facility at Darlington (DTRF) is used to extract tritium from the heavy water on a continuous basis and concentrates it in preparation for immobilization and storage. There are several internal and external hazards associated with the operation of DTRF, such as fires, onsite transportation accidents, exposure to radioactive materials, explosion due to Hydrogen (H2) and the release of radioactive substances from the Tritium Immobilization System. This paper conducts a risk analysis for the DTRF Cryogenic Refrigeration System, associated with both Low and High Tritium Distillation columns. This study includes hazard identification, consequence analysis and risk estimation. Fault Tree and Event Tree Analysis conducted, system reliability, availability and human reliability was considered. Consequently, methods for risk reduction and integration with improvement to CRS design is discussed.

Real time optimization of distillation columns using data‐driven models

AbstractThis work presents a data‐driven model of a two‐product distillation tower that is suitable for real‐time optimization (RTO) of distillation columns. The proposed model accurately predicts product mass fractions using operating variables and tray temperatures by integrating a linear data‐driven inferential composition model (based on two tray temperatures in each section of the tower, reflux/distillate ratio, and reboiler duty/bottoms flow ratio) with a neural network (NN) model that predicts tray temperatures from the value of the manipulated variables. RTO is carried out via an iterative procedure where the sensitivity matrix is initially calculated from the model and updated using plant measurements from subsequent values. A butane splitter column is presented as a case study. Our results show that the implementation of the data‐driven model‐based RTO results in a solution that is within 0.1% of the optimization solution based on the rigorous tray‐to‐tray distillation simulation.

Experimental demonstration of the production of poly(oxymethylene) dimethyl ethers from methanolic formaldehyde solutions in a closed-loop mini-plant

Poly(oxymethylene) dimethyl ethers of chain length 3-5 ( ▪ ) are discussed as synthetic diesel fuels due to their potential to significantly reduce soot emissions while presenting physicochemical properties similar to conventional diesel fuels. A recently developed process to produce ▪ directly from methanol and aqueous formaldehyde is a promising way to avoid expensive intermediates such as trioxane, methylal, or dimethyl ether. The process consists of a reactor, a distillation sequence, and a membrane unit. The first distillation column is particularly challenging due to the reactive character of the separation, the high number of components present, and the limited solubility of formaldehyde. Up to now, the feasibility of this separation has yet to be demonstrated. This work presents closed-loop experiments in a demonstration plant erected at the Campus Straubing of the Technical University of Munich with closing recycle. In the distillation step, ▪ with with very small impurities of around 800 ppm of formaldehyde was obtained. The experimental results were compared with simulations based on a reactive equilibrium stage model. The simulations of the temperature and composition profiles for the majority of the components are in line with the experiments. Although the membrane separation exhibited weaker water selectivity than in previous tests, it was sufficient to overcome distillation boundaries. Additionally, this study assesses the potential for solid precipitation, explores trade-offs in product quality, and discusses overall mass balances of the process, demonstrating the overall feasibility of the process.

Optimizing olefin purification: An artificial intelligence-based process-conscious PI controller tuning for double dividing wall column distillation

This study investigates the light olefin separation using dual dividing wall columns (DWCs) and compares various proportional-integral (PI) controller tuning methods. A novel process-conscious reinforcement learning (RL) approach, utilizing Deep Deterministic Policy Gradient (DDPG), is developed by incorporating domain-specific knowledge into the reward function to ensure compliance with critical process variables and product purity. The DDPG-based PI tuning is evaluated against traditional methods such as Aspen’s recommended initial tuning, Ziegler-Nichols (ZN), and Cohen-Coon (CC). The result shows that the DDPG method significantly enhances control stability and accuracy, reducing error by factors of 11.9, 2.3, and 1.6 compared to Aspen, ZN, and CC, respectively. Additionally, DDPG demonstrates superior energy efficiency, consuming 13% less energy than the next best method. It also reduces purification costs by up to 13.5% and CO2 emissions by 20% compared to other methods. This study highlights the potential of integrating advanced RL techniques into industrial process control, delivering substantial improvements in stability, energy efficiency, economic, and environmental performance.

Evaluation of antimicrobial activity of plant extracts and column fractions of hexane extract against Mycobacterium kansaii

The aim of this study is to assess the antibacterial efficacy of aerial parts of the Ageratum conyzoides plant extracts and column fractions against Mycobacterium kansaii. The antimicrobial experiments were performed using five doses (0.1 µg/ml, 1 µg/ml, 10 µg/ml, 100 µg/ml, 1000 µg/ml) of plant extracts and column fractions. The results showed notable inhibitory effects on Mycobacterium kansaii. The results indicated that the extract of A. conyzoides exhibited action against all the tested bacterial isolates, with the level of activity varying depending on the concentration. The hexane extract exhibited antibacterial activity, with mean zones of inhibition measuring 97.88±9.46 and 27.1±3.33, while the ethanol extract showed a mean zone of inhibition of 100.6±3.42 and 36.77±1.74 against M.kansaii, respectively. Nevertheless, the hexane extract's three column fractions exhibited average inhibition zones of 98.60±1.0 and 23.11±4.35, 90.02±10.22 and 21.94±6.77, 99.32±3.04 and 17.23±5.26 for the third, fourth, and fifth (last sample) respectively. The findings of this study provide evidence for the conventional utilization of the Ageratum plant and emphasize the necessity for further comprehensive research to explore potential alternatives to current antibacterial medications.

Distillation column optimization: A formal method using stage-to stage computations and distributed streams

This work addresses the complexities of optimizing the number of stages in a distillation column, which typically lead to challenging non-linear mixed-integer optimization problems. To simplify this, we employ distributed streams, thereby eliminating discrete degrees of freedom. To avoid sophisticated initialization procedures, the optimization problem is reformulated by employing a sequence of stage-to-stage calculations, each reduced to maintaining only the MESH (mass, equilibrium, summation, heat) equations for a single stage.Our numerical experiments show the efficiency and stability of solving the simplified optimization problem in various scenarios, including single and multiple distillation columns. For a single column scenario, we compare the accuracy of our optimization method with a full enumeration approach. Additionally, for a pressure swing flowsheet designed to separate an azeotropic mixture, we illustrate potential energy savings by optimizing a stage distribution versus using a predetermined stage distribution.

Advance industrial monitoring of physio-chemical processes using novel integrated machine learning approach

With the rapid transition of Industry 4.0 to 5.0, modern industrial physio-chemical processes are characterized by two critical challenges: process safety and the quality of the final product. Traditional industrial monitoring methods have low reliability in accuracy and robustness, and they are inefficiently providing satisfactory results. This paper introduces a novel integration technique that employs machine learning (ML) to tackle the challenges associated with real industrial monitoring in physical and industrial processes. The proposed framework integrates distributed canonical correlation analysis - R-vine copula (DCCA-RVC), global local preserving projection (GLPP), and 2-Dimensional Deng information entropy (2-DDE). The framework's ability and productivity are assessed utilizing existing approaches such as wavelet-PCA, MRSAE, and DALSTM-AE and the new proposed novel integrated machine learning-based (DCCA-RVC) approach as benchmarks for model performance. The proposed novel approach has been validated by testing it on the ethanol-water system distillation column (DC) and Tennessee Eastman Process (TEP), utilizing it as actual industrial benchmarks. The results demonstrate that the novel integration ML-technique (DCCA-RVC) T22 – GLP monitoring graphs for the fault class type 1 in the distillation column showed a (FAR) of 0 %, a (FDR) of 100 %, a precision of 100 %, F1-score of 100 % and an accuracy of 100 %. However, for the TEP process failure event 13, the (FAR) was 0 %, the (FDR) was 99 %, the accuracy was 100 %, the F1-score was 99.5 %, and the accuracy was 99.5 %.

Economical process design of reactive-extractive distillation combining variable pressure and heat integration for separating a ternary azeotropic mixture

Research and development of environmentally friendly processes that can generate significant profits has always been a focus of attention in pharmaceutical and chemical fields. In this study, a novel reactive-extractive pressure-swing distillation (REPSD) process was designed to separate tetrahydrofuran (THF)-methanol-water (TMW) ternary azeotropic mixtures. Ethylene oxide (EO) is reacted with water to form ethylene glycol (EG) in a reactive distillation (RD) column, which removes the water from the mixture. Then, the THF-methanol (TM) mixture is separated by extractive distillation (ED) and pressure-swing distillation (PSD). A multi-objective genetic algorithm (MUOGA) with total annual cost (TAC) and total capital cost (TCC) as objective functions, along with heat integration techniques were used to optimise the designed REPSD process. Optimal operating conditions to achieve maximum economic benefits were obtained for the process. Subsequently, the environmental benefits of the process were evaluated. The results showed that the proposed REPSD with heat integration can significantly reduce the TAC and CO2 emissions by 29.1 and 26.9 %, respectively, compared to existing optimal heat integration extractive distillation (HIED) processes. This study provides a new perspective on the separation and purification of ternary azeotropic mixtures.

Energy and Economic Evaluation of Wall Placement for Divided Wall Distillation Columns

Energy and Economic Evaluation of Wall Placement for Divided Wall Distillation Columns

Research on multi-objective energy-saving optimization of propylene distillation column

The energy-saving optimization problem of the propylene distillation column was investigated. A PRO/II process simulation model was established based on the actual operating data of the unit and a sensitivity analysis of the operating parameters was conducted. On this basis, a multi-objective optimization model for the unit was established using regression analysis. The penalty function mechanism and dynamic search factor were incorporated into the MOGWO algorithm to solve the optimization model. The optimization results demonstrate that by optimizing multiple sets of operating parameters, the propylene distillation column can increase its production while reducing its energy consumption, achieving energy-saving optimization for the unit. This method provides a new theoretical basis for the energy-saving optimization design of propylene distillation units and other distillation processes.

Design and optimization of reaction-separation-recycle systems using a pseudo-transient continuation model

The reaction-separation-recycle (RSR) systems are an important part of chemical processes. Due to the interaction between reaction and separation sections, the behavior of RSR processes becomes highly complex and non-linear, posing different challenges for their design and optimization. The purpose of this study is to design and optimize RSR processes for irreversible liquid phase reaction systems using the pseudo-transient continuation (PTC) approach within an equation-oriented programing environment. This approach was applied to investigate one binary system and four ternary systems. The differential-algebraic models of the proposed process flowsheets were solved until the steady-state conditions were reached. The tray bypass efficiency method was incorporated into the PTC to circumvent the need for discrete optimization. The results demonstrated that in those cases where the product was heavier than the reactants, employing a stripping column was more economical than using a conventional distillation column for both the binary and ternary systems. In the ternary systems with two recycle streams, when the product was the intermediate component in terms of boiling point, the utilization of a divided-wall distillation column (DWC) resulted in a total annual cost saving of 35 % and 41 % compared to direct and indirect separation methods, respectively.

HPLC analysis and antimicrobial activity of Vitex altissima leaves extracts

The exploration of ethnomedicinal plants for phytochemical and pharmacological properties is crucial for developing novel therapeutics for chronic diseases. Vitex altissima, known as the Peacock chaste tree, is a significant member of the Verbenaceae family, is widely utilized in traditional medicine. This study investigates the phytochemical composition and antibacterial properties of V. altissima, a plant recognized for its medicinal use by various indigenous communities, including the Malayali tribes of Servarayan hills. The study involved extracting phytochemicals from the leaves of V. altissima using hexane, ethyl acetate and methanol through soxhlet extraction. The extracts were analyzed for secondary metabolites, including carbohydrates, proteins, amino acids, steroids, glycosides, alkaloids, tannins, phenolics, flavonoids, terpenoids, saponins, anthraquinones, oils and resins, diterpenes, phlobatannins and coumarins. Quantitative assessments of phenolic compounds, flavonoids and alkaloids were performed. Column chromatography was employed to fractionate the methanolic extract, resulting in 12 distinct fractions. These fractions were screened for antibacterial activity against E. coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus mutans and Staphylococcus aureus using the agar well diffusion method. The minimal inhibitory concentration (MIC) was determined through a 2-fold serial dilution technique. High-performance liquid chromatography (HPLC) was utilized to identify the polyphenolic compounds responsible for antibacterial activity. Phytochemical screening revealed a diverse array of bioactive compounds in the extracts, with methanol extracts showing the highest total phenolic content (218 ± 7.21 mg GAE/g of extract) and significant antibacterial activity. The ethyl acetate extract exhibited the highest total flavonoid content (40.67 ± 6.65 mg QE/g of extract). Among the fractions, the column fraction from the chloroform-methanol gradient (VACF) demonstrated superior antimicrobial activity, particularly against S. mutans and P. aureginosa. The MIC values for VACF were 427 µg/mL for P. aeruginosa and 400 µg/mL for S. aureus, indicating potent antibacterial properties. HPLC analysis identified key polyphenolic compounds, including p-coumaric acid, ferulic acid and elagic acid, as the primary contributors to the antibacterial activity. The presence of these compounds aligns with the observed antimicrobial efficacy and highlights the potential of V. altissima extracts as a source of natural antibacterial agents.

CFD-based shape optimization of structured packings for enhancing separation efficiency in distillation

Free-form shape optimization techniques are investigated to improve the separation efficiency of structured packings in laboratory-scale distillation columns. A simplified simulation model based on computational fluid dynamics (CFD) for the mass transfer in the distillation column is used and a corresponding shape optimization problem is formulated. The goal of the optimization is to increase the mass transfer in the column by changing the packing's shape, which has been previously used as criterion for increasing the separation efficiency of the column. The computational shape optimization yields promising results, with an increased mass transfer of nearly 20%. For validation, the resulting optimized shape is additively manufactured using 3D-printing and investigated experimentally. The experimental results are in good agreement with the performance improvement predicted by the computational model, yielding an increase in separation efficiency of around 20%.

On the selection of control structures using process operability analysis

This work aims to develop a generalizable framework for control structure selection using process operability analysis. Current approaches for selecting controlled variables in chemical processes are limited to assessing system attributes individually, focusing on controller performance or the economic impact based on a constant setpoint policy. However, the competitive industrial manufacturing market requires a holistic approach for control structure selection in large-scale plants that takes into account multiple factors. In particular, process operability can help to enable a generalizable approach that is able to select control structures that are operable considering economic and performance factors simultaneously. To achieve this goal, a framework that uses the Operability Index (OI) as a metric for ranking the achievability of the control objectives for the selected control structures is developed. To test the framework, a depropanizer distillation column is investigated as a case study associated with large-scale energy systems. This work thus introduces novel formulations and algorithms for the control structure selection problem, enhancing the design, operations, and synthesis of existing and future industrial systems.

Decentralized control of ideal ternary reactive distillation column with inert

Decentralized control of an ideal hypothetical ternary reactive distillation column with an inert component is explored. Both composition measurement based and temperature inferential control structures are designed using simple heuristic approaches (two and three-point). The three-point composition control structure is proposed for the example RD column for the first time in this work. Although stable closed loop responses are seen for the throughput changes for all, the two-point structures have failed to achieve tight control of the product purity in the bottoms or purity of inert in the distillate for the inert composition changes due to fixed reflux ratio. The performances of the three-point control structures for the inert composition changes are quite satisfactory due to the indirect manipulation of the reflux ratio. The independent manipulation of the reflux rate and distillate (instead of fixed reflux ratio policy) is an important control decision for the successful regulation of the example RD column.

Level sets of nonsmooth functions, Part 2: Lipschitz and piecewise-differentiable manifolds

This paper introduces piecewise-differentiable (PCr) manifolds according to a unified general framework that also applies to nonsmooth Lipschitz manifolds and smooth manifolds. We present definitions of nonsmooth manifolds and embedded submanifolds for abstract sets as well as for subsets of Rn, and explore the relationships between them. The PCr and Lipschitz Rank Theorems from Part 1 of this series, in terms of the Clarke Jacobian and B-subdifferential generalized derivative sets, are used to characterize level sets of functions between nonsmooth manifolds as embedded submanifolds. We illustrate how the Level Set Theorems developed in this paper can be applied to functions on Euclidean space, including a piecewise-differentiable process model for distillation columns.

Energy-efficient optimization design of bio-butanol fermentation broth purification process

To address the downstream processing challenges of the IBE (isopropanol-butanol-ethanol) system and obtain biobutanol products with a mass fraction of 0.9999, as well as obtain a mass fraction of 0.99975 for the IE (isopropanol-Ethanol) mixture product as a gasoline additive, this study proposes a four-column distillation process termed "Dehydration-Butanol-Extractive Four Column Distillation" (DBE-4CD). With the heat load as the optimization target, the DBE-4CD process was optimized to determine the optimal operating parameters. Based on the optimized process and considering the energy-saving potential of the dividing wall column, an “Azeotropic Dividing Wall-Extractive Three Column Distillation” (ADE-3CD) process was subsequently proposed to further enhance energy efficiency and reduce consumption. Compared to both conventional literature process and the DBE-4CD process, the total load of the ADE-3CD process decreased to 7433.5 kW, representing reductions of 20.35% and 10.11%, respectively. Additionally, the mass recovery rates of butanol and the IE mixture reached 99.90% and 99.64%, respectively, exceeding those of the conventional literature process, which were 99.11% and 99.18%.

Mixing efficacy and residence time distribution measurement in heavy water upgradation plant using radiotracer

To evaluate the efficiency of catalyst packing and to characterize the liquid phase flow dynamics in a heavy water distillation column, a series of residence time distribution (RTD) measurements were carried out using radiotracer. For a RTD measurement, about 1 mCi (37 MBq) of 82Br as ammonium bromide was injected as an impulse and its movement was monitored by thirty NaI(Tl) detectors at the inlet and outlet of the five catalyst cages present in the distillation column. The tracer measurements were carried out at varying liquid phase flow rate from 80 to 180 LPH. From the radiotracer data, it was concluded that radial distribution of the liquid phase at the top and bottom of the five cages was uniform. However, a slight non uniformity was observed in the middle cages. The residence time of the heavy water in the five cages varying the liquid flowrate was found to be varying from 4.2 min to 36.7 Min. To characterize the liquid mixing in each section of the distillation column, axial dispersion model was used using convolution method. The model parameter peclet number (Pe) was used to characterize the mixing. The Pe values for the five sections were found to be varying from 7 to 35. The Pe value suggests that flow of the liquid phase in the column was close to the plug flow in bottom three sections as desired.

Development of the gas–liquid drag model for flows on sieve plates in distillation columns via direct numerical simulation

Development of the gas–liquid drag model for flows on sieve plates in distillation columns via direct numerical simulation