Design Of Distillation Research Articles

Comparative study of conventional and process intensification by reactive distillation designs for glycerol carbonate production from glycerol and diethyl carbonate

Glycerol carbonate (GC) can be produced from glycerol (GL), a low-value byproduct in the biodiesel industry. In this work, continuous processes of GC production via transesterification from crude GL and diethyl carbonate (DEC) were developed using Aspen Plus. Two cases were considered, and their process performances were compared. In Case I, a conventional design consisted of a continuously stirred tank reactor for the reaction section and a distillation column for the purification section. In Case II, a process intensification design consisted of a reactive distillation column that could accommodate both reaction and purification within a single column. In both cases, the process optimizations were carried out by connecting the process models in Aspen Plus to MATLAB, using the Genetic Algorithm as the optimizer. The results showed that Case II was superior to Case I in terms of energy utilization, CO2 emissions, and economics with the specific energy consumption of 1.92 kWh/kg of diethyl carbonate, % internal rate of return of 274, payback period of 1.44 years, and CO2 emissions of 0.26 kg CO2/kg DEC. Lastly, the proposed process in Case II was compared with the GC production using dimethyl carbonate (DMC). It was found that using DEC was superior to DMC due to easier separation and glycidol avoidance.

Multi-objective optimisation and energy-saving design of pressure-swing extractive distillation for separating ethanol/acetonitrile/water azeotropic system

Multi-objective optimisation and energy-saving design of pressure-swing extractive distillation for separating ethanol/acetonitrile/water azeotropic system

Pilot-scale validation and process design of reactive distillation with HY@SiC structured catalytic packing for dimethyl maleate production

In reactive distillation processes constrained by the efficiency of vapor–liquid mass transfer, reducing the physical dimensions of simultaneous reaction and separation operations can significantly enhance overall process efficiency. To achieve this, HY@SiC, a structured catalytic packing was developed, allowing for concurrent catalytic reactions and vapor–liquid mass transfer on its surface. This innovative catalytic packing was employed in a pilot reactive distillation column to experimentally investigate the production of dimethyl maleate via reactive distillation, marking the first validation of its performance. Additionally, a reactive distillation model incorporating HY@SiC structured catalytic packing was established. Ultimately, a new double-column reactive distillation process for producing dimethyl maleate was proposed, with plans for optimization aimed at minimizing total annual cost, which was 4.08 × 105 $/y, utilizing a sequential iterative algorithm as the core focus for design improvements, while achieving DMM yield and purity levels of 99.42 % and 99.0 wt%, respectively.

Novel sustainable design of pressure-swing distillation coupled with natural decanting and Organic Rankine Cycle for separating n-propanol/benzene/water mixture

The separation of ternary azeotropic mixtures is a common challenge in the chemical industry due to the unique properties of azeotropes. This phenomenon complicates the separation processes, as traditional distillation methods may not effectively separate the components. The present work introduces a novel pressure-swing distillation (NPSD) process integrated with natural decanting for the effective separation of an n-propanol/benzene/water mixture. By exploiting the liquid–liquid envelope characteristics of the mixture, phase separation prior to distillation significantly enhances the separation efficiency. The NPSD process was optimised using the NSGA-II, addressing economic, environmental, and energetic objectives. Key findings reveal that the implementation of decanting allows the NPSD process to operate without substantial pressure adjustments, thereby facilitating fine separation more efficiently than conventional pressure-swing distillation designs. The proposed NPSD process can achieve up to 25.76 % savings in TAC while reducing CO2 emissions and improving energy efficiency. Furthermore, the integration of mechanical vapour recompression heat pump and Organic Rankine Cycle systems enhances energy saving, resulting in a TAC reduction of up to 31 % and a decrease in CO2 emissions of up to 38 % compared to existing energy-efficient designs. These findings highlight the potential of the NPSD-MVR-ORC system for sustainable chemical separation processes.

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.

Single-pot extractive double divided wall column for Acetonitrile, Ethanol, and water Separation: Design and control

An integrated single-pot double divided wall column (DDWC) process is designed for separating acetonitrile, ethanol and water into its constituent pure components using dimethyl sulfoxide (DMSO) as the homogenous extractive entrainer. Comparison with the recently reported side-stream extractive distillation (SSED) design with three columns shows the single-pot process to be more cost-effective and energy-efficient. The SSED process has a Total Annualized Cost (TAC) that is higher by 9.91% and an energy consumption that is higher by 7.27%. Also, a decentralized temperature inferential control system is shown to effectively reject large throughput and moderate feed composition changes to establish the controllability of the proposed design. The developed single-pot process is thus a promising alternative, particularly for space-constrained production facilities.

Optimization of vacuum membrane distillation and advanced design of compact solar water heaters with heat recovery

With the escalating global population, freshwater scarcity has become a pressing challenge. To address this issue, this paper presents a novel compact solar water heater (CSWHT) system incorporating heat recovery and submerged-membrane filtration. The research aims to develop an efficient desalination method that reduces water production costs and maximizes output. The system comprises a feed storage tank, a vacuum membrane distillation (VMD) system positioned atop the submerged CSWHT, a liquid ring vacuum pump (LRVP) utilizing waste heat from the outlet to preheat the feed, and hollow fiber membranes.Three critical parameters, namely the mass of the feed in the storage tank, the number of hollow fiber membrane rows, and the feed volumetric flow rate, were optimized using a multi-objective non-dominated sorting genetic algorithm II (NSGA II) to minimize water production cost (WPC) while maximizing water production. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed to identify the optimal scenario from among the selected best solutions.For Mediterranean seawater and a 6 m2 solar collector area, the multi-objective NSGA II algorithm identified 20 membrane rows per collector, 334.4 kg feed mass in the storage tank, and 0.027 kg.s−1 feed mass flow rate as the optimal system parameters. Under these optimized conditions, the system exhibited a daily production capacity of 92.5 kg water, 4.6 kg chloride, and 0.03 g bromide, while achieving a production cost of 12.2 USD.m−3 and a gain output ratio (GOR) of 1.6. These results highlight the system's remarkable potential for desalination with high efficiency and low cost. Future research should focus on scaling up the system and exploring its applicability in various geographical locations.

Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system

This study explores two scenarios for optimizing the predictive control of flux in water desalination systems: experimental design of direct contact membrane distillation (DCMD) and artificial intelligence (AI) models. Deep learning networks, specifically Long Short-Term Memory (LSTM), and standalone AI models, including hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), were evaluated. Evaluation criteria, along with 2D graphical visualization, were used to assess model accuracy. ANFIS-C4 emerged as the standout model, achieving perfect predictive skills with 100 % accuracy and low RMSE of 0.0522 in the training phase, and maintaining high performance in testing with 99.73 % accuracy and RMSE of 0.7121. Similarly, ANN-C4 demonstrated near-perfect accuracy with 100 % accuracy and RMSE of 0.0643 in the training phase and also maintained excellent performance in the testing phase. Despite requiring multiple inputs, ANFIS and ANN show remarkably high capability in predictive accuracy. Among LSTM models, LSTM-C3 achieved the highest training DC of 91.35 %, indicating robust performance in capturing training data variability. LSTM-C2 showed superior generalization with a testing DC of 0.8539, despite using only two input parameters. The narrow distribution of predicted values around the median in violin plots for ANFIS-C4 and ANN-C4, along with their low RMSE, validates their superior performance. The exceptional results of ANFIS-C4 and ANN-C4 highlight their potential in water desalination applications, supporting Sustainable Development Goal 6 (SDGs-6) and Environmental Protection Agency (EPA) objectives for sustainable water management. This research underscores the importance of advanced computational models in enhancing the efficiency and reliability of water desalination systems, contributing to global efforts in sustainable water resource management.

Evaluating the performance of spherical, hemispherical, and tubular solar stills with various configurations - A detailed review

Solving global water shortages has become an urgent challenge, hindering sustainable development. Therefore, comparing different solar still designs from application and economic perspectives is necessary. Solar distillation is considered a major innovation in the alternative energy sector for purifying brackish or brine water into clean water. Despite the extensive literature on improved solar stills, determining the most efficient designs for residential and industrial applications remains difficult. This review compares the productivity of spherical, hemispherical, and tubular solar still designs. The aim is to study the factors that influence the efficiency of each type and to analyze recent research and results obtained under different conditions. The results show that innovations in solar distillation design can take many forms to improve efficiency and productivity. For example, adding parabolic mirrors can increase productivity in spherical, hemispherical, and tubular stills by 35 to 70%. Likewise, innovative designs such as rotating spheres and changing bowl shapes significantly increased the productivity of spherical and hemispherical stills. Likewise, retrofitting a still with vacuum generation technology can significantly increase yields by 50 to 70%. In addition, using nanomaterials, especially nanophase change materials (NPCM), has increased the efficiency of the spherical and tubular stills by 116.5%, producing 7.62 kg/m2 per day. Therefore, the NPCM-equipped model was still the most efficient option among the three designs.

Design and optimization of reactive distillation for enhancing supercritical transesterification process to produce biodiesel

The present study explored the optimization of a reactive distillation column (RDC) for biodiesel production using supercritical transesterification (SCTE). The SCTE process at high pressure and temperature, is known for water-sensitive flexibility and catalyst-free operation, and has shown promising advantages in biodiesel production. The steady-state simulations were conducted using Aspen Plus, with two RDC configurations: RDC-1, employing a single feed of oil and methanol, and RDC-2, utilizing separate feeds. Surprisingly, high conversion rates were achieved at only 8.5 MPa pressure. The study aiming to identify an optimized RDC design, systematically examined the impact of design parameters such as reflux ratio, feed temperature, and the number of reactive stages on conversion and energy requirements. Internal column specifications and species flow through each stage were investigated to comprehend reaction and separation processes. Temperature analysis revealed that preheating to 380 °C significantly improved conversion and reduced reboiler heat duty. RDC-1, with 9 reactive stages, demonstrated greater conversion efficiency, while RDC-2, with 11 stages, exhibited better biodiesel separation. By optimizing reflux ratios, the study achieved remarkable conversions with enhanced methanol separation. Cost estimation revealed that RDC-1 promoted lower capital and operating costs, making it a preferred design and an efficient option for SCTE-based biodiesel production.

Sea Water Distillation Design Using Renewable Energy for Drinking Water

The increase in drinking water needs in Ternate City is ttriggered by very rapid population growth. Population growth has resulted in the conversion of forests into housing which has resulted in reduced water catchment areas. In the end, several locations experienced a decline in water quality from fresh to brackish. To overcome the decline in the volume and quality of clean water, a plan is being made to distill sea water into fresh water. The method used in this research is the experimental method. Solar desalination is a process where solar energy is utilized to putify the fresh water from saline/brackish water for drinking purposes, in charging of the batteries, research laboratories. Experimental results with a distillation flow area of 325 cm with an average volume of 43.7 ml. Tests with a distillation drain area of 650 cm produced 76.0 ml of fresh water. Experiments with a distillation flow area of 975 cm produced an average of 106.3 ml of fresh water. The effectiveness of distillation occurs at the widest drainage. The price of fresh water per milliliter is IDR 1.41 cheaper than bottled water, namely IDR. 6.66 per milliliter. The results of research on the salinity of salt water to fresh water show that the salinity of salt water is 35 ppt, while the salinity of distilled water is 0, this shows that the result of distillation is fresh water that can be consumed.

Energy-saving design of azeotropic distillation for the separation of methanol–methyl methacrylate azeotrope from industrial effluent

For the purpose of separating the binary methanol–methyl methacrylate (MMA) azeotrope in light boiling residues and preventing MMA polymerization from acetone cyanohydrin process, low pressure heterogeneous azeotropic distillation (HAD) with process intensification was adopted. The binary interaction parameters of the azeotrope were obtained by correlating and regressing the experimental data of isobaric vapor–liquid equilibrium at low pressures (75, 50 and 26 kPa for methanol-MMA and 26 kPa for cyclohexane-MMA). The residue curve map at 26 kPa was plotted under the UNIQUAC model, and the optimal parameters of its conventional process were achieved through sequential iterative optimization procedure. The purity of MMA and methanol was 99.99 wt% and 99.90 wt%, respectively. Subsequently, in order to further reduce costs and energy consumption, dividing wall column (ADWC), heat pump distillation (HP-AD), and heat pump assisted azeotropic dividing wall column (HP-ADWC), three technical approaches were explored and compared. Total annual cost (TAC), total energy consumption (TEC), and CO2 emissions were used as evaluation indicators. Among them, ADWC reduced its annual capital investment by 14.48 % through special structural design. HP-AD effectively reduced TEC by 27.30 %. HP-ADWC combined the advantages of both, effectively reducing costs and saving energy (12.88 % in TAC). At present, the purity and energy saving of the product meet industrial expectations, providing valuable insights for enhanced methanol-MMA azeotrope recovery from industrial effluent.

Optimal design and scheduling of ecofriendly process for the production of natural tocopherols present in residue from the vegetable oil refinery

This paper addresses the optimal design of molecular distillation (MD) process for the treatment of a residue of the edible oils refining industry. This work focuses on optimizing the number of units and their size, performing the scheduling of the separation tasks assigned to each unit with the objective of treating a residue with the minimum number of process equipment at a specific size to process a certain annual production of ODD (Oil Deodorization Distillate). The MD is considering clean technology because only consumption electric energy and not generate highly polluting waste. The stream of ODD has a high organic contaminant load. This requires more than one distillation stage to obtain tocopherols as a product (four stage for this study case). Here, to evaluate the process with minimum number stage a first screening was done using the traditional model of constant size factors, but afterwards the best candidate alternative was optimized as regards to the flow rates to be recycled to previous stages. The objective is to add value to a residual stream of oil refining processes, resorting to MD technology, given that MD is a very expensive technology, and in this particular case where its size is small, limited by manufacturer. For all scheduling alternatives analyzed, the best economic performance yields a Profit of 381,360 $/yr with two MD equipment and optimizing the material balances and recycles. This process is with an assignment of tasks to units that yields one larger equipment to perform the first three separation stages, and a quite small equipment to perform the last separation stage. This process has even better economic performance than the same process without reflux recirculation optimization. The process with one unit yielded technically infallible results. The process with three units showed less favorable economic performance.

A review of preparing high-purity metals by vacuum distillation

With the development of science and technology, the high-tech industry has increasingly higher requirements for the purity of metals. However, the presence of trace impurities can have a bad effect on the properties of metals. Vacuum distillation process is widely used to prepare high-purity metals because of its advantages of high metal recovery, good environmental protection and simple equipment. In this paper, the overall progress in the preparation of high-purity metals around the principle and process research of vacuum distillation is systematically summarized. Principles of vacuum distillation, variant design of vacuum distillation, parameter optimization and simulation are covered. Based on the current status of the preparation of high-purity metals by this technology, it is pointed out that mechanistic studies and simulations are indispensable in scientific research. This research has certain reference significance for improving production efficiency, saving production cost and improving metal purity.

Effect of feed water temperature on the performance and economics of thermal energy driven multiple effect distillation system for water treatment

This study presents the design, fabrication, and experimental analysis of Multiple Effect Distillation (MED) plant with four effects (4+C system). A total of 15 experiments were conducted, systematically categorized based on feed water temperature. Baby fire tube boiler producing 25 kg/h steam were installed with plant for experimental study. The investigation focused on distillate output, Gain Output Ratio, Overall Heat Transfer Coefficient, and the cost of distillate. Investigation demonstrates a notable increase in distillate output and Gain Output Ratio as the feed water temperature increases. Furthermore, improvements in the Overall Heat Transfer Coefficient with higher temperatures was observed. Notably, the cost of distillate decreased from $2.5/m³ to $1.5/m³ as the temperature increased. Findings suggest that 80 °C feed water temperature optimizes distillate output and Gain Output Ratio. Beyond this temperature, a decrease in distillate production occurs due to reduced latent heat of vaporization. The economic analysis reveals a trade-off between distillate output and cost, emphasizing the importance of optimizing feed water temperature. The inlet temperature was maintained at 115 ± 0.3 °C, while the boiler pressure remained at 5 bar. In the pilot plant configuration, MED system operating at 80 °C generated a daily water output of 438.93 L/day. The associated cost for producing fresh water was1.65 $/m3, and the system achieved a maximum Gain output ratio of 1.95.

Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning

Continual learning (CL) has shown promising results and comparable performance to learning at once in a fully supervised manner. However, CL strategies typically require a large number of labeled samples, making their real-life deployment challenging. In this work, we focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories. We provide a comprehensive analysis of SSCL and demonstrate that unreliable distributions of unlabeled data lead to unstable training and refinement of the progressing stages. This problem severely impacts the performance of SSCL. To address the limitations, we propose a novel approach called Dynamic Sub-Graph Distillation (DSGD) for semi-supervised continual learning, which leverages both semantic and structural information to achieve more stable knowledge distillation on unlabeled data and exhibit robustness against distribution bias. Firstly, we formalize a general model of structural distillation and design a dynamic graph construction for the continual learning progress. Next, we define a structure distillation vector and design a dynamic sub-graph distillation algorithm, which enables end-to-end training and adaptability to scale up tasks. The entire proposed method is adaptable to various CL methods and supervision settings. Finally, experiments conducted on three datasets CIFAR10, CIFAR100, and ImageNet-100, with varying supervision ratios, demonstrate the effectiveness of our proposed approach in mitigating the catastrophic forgetting problem in semi-supervised continual learning scenarios. Our code is available: https://github.com/fanyan0411/DSGD.

UniADS: Universal Architecture-Distiller Search for Distillation Gap

In this paper, we present UniADS, the first Universal Architecture-Distiller Search framework for co-optimizing student architecture and distillation policies. Teacher-student distillation gap limits the distillation gains. Previous approaches seek to discover the ideal student architecture while ignoring distillation settings. In UniADS, we construct a comprehensive search space encompassing an architectural search for student models, knowledge transformations in distillation strategies, distance functions, loss weights, and other vital settings. To efficiently explore the search space, we utilize the NSGA-II genetic algorithm for better crossover and mutation configurations and employ the Successive Halving algorithm for search space pruning, resulting in improved search efficiency and promising results. Extensive experiments are performed on different teacher-student pairs using CIFAR-100 and ImageNet datasets. The experimental results consistently demonstrate the superiority of our method over existing approaches. Furthermore, we provide a detailed analysis of the search results, examining the impact of each variable and extracting valuable insights and practical guidance for distillation design and implementation.

Design and energy-saving strategy of sustainable pressure-swing distillation with thermally and electrically coupled intensification for separating ternary mixture with multiple azeotropes

The separation of ternary mixtures that form multiple azeotropes is frequently encountered in industry. Benefiting from the pressure-sensitive nature of the azeotropic mixture, the pressure-swing distillation (PSD) is promising to realise the separation without introducing foreign solvents. However, the wide application of PSD seems challenging from an energetic point of view. This work has developed a sustainable PSD process with thermally and electrically coupled intensification to decrease energy consumption, taking the separation of homogeneous acetonitrile/ethanol/water mixture as a demonstrating example. Two conventional PSD processes with different product sequences are designed through the ternary phase diagram and further optimised using the NSGA-II, considering multiple objectives, including total capital cost, total operating cost, and CO2 emissions. The optimal solution is selected by the TOPSIS analysis for each sequence and is then intensified with thermal heat integration and electricity-driven heat pump techniques. The results show that, in comparison to the non-heat-integrated PSD process, the proposed intensified design can dramatically reduce the total annual cost by up to 48%, simultaneously decreasing CO2 emissions by up to 60% and boosting energy efficiency. The present study can be of reference for designing a sustainable PSD process in ternary azeotropic separation.

Improving Threat Mitigation Through a Cybersecurity Risk Management Framework: A Computational Design Science Approach

ABSTRACT Cyberattacks have been increasing in volume and intensity, necessitating proactive measures. Cybersecurity risk management frameworks are deployed to provide actionable intelligence to mitigate potential threats by analyzing the available cybersecurity data. Existing frameworks, such as MITRE ATT&CK, provide timely mitigation strategies against attacker capabilities yet do not account for hacker data when developing cyber threat intelligence. Therefore, we developed a novel information technology artifact, ATT&CK-Link, which incorporates a novel transformer and multi-teacher knowledge distillation design, to link hacker threats to this broadly used framework. Here, we illustrated how hospital systems can use this framework to proactively protect their cyberinfrastructure against hacker threats. Our ATT&CK-Link framework has practical implications for cybersecurity professionals, who can implement our framework to generate strategic, operational, and tactical cyber threat intelligence. ATT&CK-Link also contributes to the information systems knowledge base by providing design principles to pursue targeted cybersecurity analytics, risk management, and broader text analytics research through simultaneous multi-modal (e.g., text and code) distillation and classification.

Model-Guided Generative Adversarial Networks for Unsupervised Fine-Grained Image Generation

Unsupervised fine-grained image generation is a challenging issue in computer vision. Although many recent significant advances have improved performance, the ability to synthesize photo-realistic images in an unsupervised manner remains extremely difficult. The existing methods compose an image via complex three-stage generative adversarial networks and impose constraints between the latent codes. This pipeline focuses on the disentanglement and ignores the quality of generated images. In this article, we propose a novel two-stage approach for unsupervised fine-grained image generation, termed Model-Guided Generative Adversarial Networks (MG-GAN). We introduce an attention module for exploring the correlation between fine-grained latent codes and image features in the foreground generation stage. The attention module enables the network to automatically focus on the color details and semantic concepts of objects related to different fine-grained classes. Furthermore, we incorporate knowledge distillation strategy and design a simple but effective inverse background image generator as a teacher to guide the background image generation. With the help of knowledge learned in the pre-trained inverse background image generator, a comfortable canvas is synthesized and combined with foreground object more reasonably. Extensive experiments on three popularly fine-grained datasets demonstrate that our approach achieves state-of-the-art performance and is even competitive with semi-supervised method.