Search Results (79)

The theoretical modeling of a dynamic system will have derivatives of the response (y) with respect to time (t). Two common physical attributes (i.e., parameters) of dynamic systems are dead-time (θ) and lag (τ). Theoretical dynamic modeling will contain physically interpretable parameters such as τ and θ with physical constraints. In addition, the number of unknown model-based parameters can be considerably smaller than empirically based (i.e., lagged-based) approaches. This work proposes a Theoretically based Dynamic Regression (TDR) modeling approach that overcomes critical lagged-based modeling limitations as demonstrated in three large, multiple input, highly dynamic, real data sets. Dynamic Regression (DR) is a lagged-based, empirical dynamic modeling approach that appears in the statistics literature. However, like all empirical approaches, the model structures do not contain first-principle interpretable parameters. Additionally, several time lags are typically needed for the output, y, and input, x, to capture significant dynamic behavior. TDR uses a simplistic theoretically based dynamic modeling approach to transform x_t into its dynamic counterpart, v_t, and then applies the methods and tools of static regression to v_t. TDR is demonstrated on the following three modeling problems of freely existing (i.e., not experimentally designed) real data sets: 1. the weight variation in a person (y) with four measured nutrient inputs (x_i); 2. the variation in the tray temperature (y) of a distillation column with nine inputs and eight test data sets over a three year period; and 3. eleven extremely large, highly dynamic, subject-specific models of sensor glucose (y) with 12 inputs (x_i). Full article

Tetrahydrofuran (THF) and methanol (MeOH) are widely used as organic solvents in chemical, pharmaceutical, and other industrial fields. The wastewater from producing 1,4-butanediol contains THF, MeOH, and water ternary azeotropic mixture. In this study, to protect the environment and improve economic feasibility, THF and MeOH from the wastewater must be recovered. Triple-column extractive distillation (TED), pressure-swing azeotropic distillation (PSAD) and reactive extractive dividing-wall column (REDWC) are introduced to separate this ternary system, and the NSGA-III algorithm is introduced to optimize the processes, taking the total annual cost (TAC), CO₂ emissions, and process route index (PRI) as objective functions. The results indicate that in comparison with TED process, TAC of PSAD and REDWC is reduced by 29.92% and 24.25%, respectively, and CO₂ emissions decreased by 18.01% and 25.13%, while PRI increased by 150.25% and 100.50%. This study can provide an insight for the design of ternary azeotropic system separation. Full article

Investors demand risk minimization references or at least demonstrator plant operations that are scaled down by a factor of about 25 times less than the manufacturing scale. This causes increased investments of about 30% and a time delay of about 3–5 years. Nevertheless, modern process simulation studies based on experimental model parameter determination at a reduced laboratory scale and process model validation by mini-plant operations with risk assessment studies based on a statistically sound quality by design (QbD) approach should be able to substitute existing methods with less effort in terms of time and cost. This approach is used for a risk assessment study based on an industrial-scale simulated moving bed chromatography separation of m- and p-isomers, including potential enrichment cycles of the simulated moving bed’s (SMB) internal desorbent and the corresponding raffinate and extract distillation columns, and well-documented experimental literature data. The results quantify potential risks within probability ranges for investor decisions quite sufficiently. The benefits of ROI across various annual capacity scales and product magnitudes are evident through reductions of about 30% regarding investment and 3–8 years in terms of time to market, which should motivate the desire to implement these innovative methods more strategically in industrial daily work instead of piloting demonstrator-scale construction and operation. Full article

Fusel oil is a fermentation by-product composed of a complex mixture of alcohols (ethanol, isoamyl, propanol, and butanol isomers) and water. The primary challenges lie in water separation and the recovery of the valuable component, isoamyl alcohol. In this work, we demonstrate an efficient separation process using a non-polar, non-toxic, water-immiscible solvent, namely hexane, to reduce the water content of fusel oil from an initial 14 wt.% to 1.46 wt.% at a solvent to fusel oil ratio of 1:1 and to 0.55 wt.% at a 4:1 ratio. The proposed separation process was designed with a 1:1 ratio to minimize equipment size. In the first step, a decanter vessel enabled phase separation, followed by two distillation columns. The bottom product from the second column achieved a purity of 99.29 wt.% isoamyl alcohol (97.91 wt.% isomers and 1.38 wt.% hexanol) with a recovery rate of 97.33%. The distillate flows were directed to the second decanter vessel, recovering 99.665% of hexane. This study confirms the effectiveness of the proposed process in separation of highly valuable isoamyl alcohol from fusel oil via a hybrid decanter–distillation scheme. The proposed process attains a specific energy consumption in the reboilers of 0.65 kWh per kilogram of product (equivalent to 1.21 kg of steam per kilogram of product). This represents a notable improvement compared to the configuration reported by other authors for the separation of isoamyl alcohol using divided-wall columns (DWC), which requires 2785 kJ per kilogram of product (i.e., 0.774 kWh per kilogram of product). An economic analysis was performed to compare the process of separating isoamyl alcohol from fusel oil using the minimum hexane ratio (1:1) and the maximum ratio (4:1). All cost values increased significantly with higher solvent ratio. Remaining challenges include the purification of waste aqueous streams and future valorization of the hexane–alcoholic mixture. Full article

The separation of butadiene 1,3 from pyrolysis products is a critical step in the petrochemical industry, as butadiene is a key raw material for producing synthetic rubber and other polymers. This study presents a detailed model-based analysis of the separation process, focusing on optimizing operational parameters to maximize butadiene recovery, enhance product purity, and reduce energy consumption. The simulation was conducted using Aspen Plus, evaluating critical variables such as the solvent-to-feed ratio, reflux ratio, number of column stages, and energy integration between distillation units. The simulation results indicated that an optimal solvent-to-feed ratio of 1.5:1 and a reflux ratio of 4.2:1 in the extractive distillation column provided the highest separation efficiency. Under these conditions, the recovery rate of butadiene 1,3 reached 98%, with a final product purity of 99.5%. Furthermore, this study revealed that increasing the number of theoretical stages in the distillation column improved the separation process without significantly increasing energy demand. Energy integration, specifically through heat recovery between the primary distillation and extractive distillation columns, led to a 12% reduction in total energy consumption. These findings demonstrate the importance of fine-tuning operational parameters to achieve high separation efficiency and product quality while minimizing energy use. This model-based analysis provides valuable insights into the design and optimization of industrial-scale butadiene separation processes, offering strategies to reduce operational costs and improve sustainability in production. The methodology and results can serve as a basis for further improvements in similar separation processes across the petrochemical industry. Full article

This review provides a comprehensive overview of the evolution and application of artificial intelligence (AI) and large language models (LLMs) in engineering, with a specific focus on chemical engineering. The review traces the historical development of LLMs, from early rule-based systems and statistical models like N-grams to the transformative introduction of neural networks and transformer architecture. It examines the pivotal role of models like BERT and the GPT series in advancing natural language processing and enabling sophisticated applications across various engineering disciplines. For example, GPT-3 (175B parameters) demonstrates up to 87.7% accuracy in structured information extraction, while GPT-4 introduces multimodal reasoning with estimated token limits exceeding 32k. The review synthesizes recent research on the use of LLMs in software, mechanical, civil, and electrical engineering, highlighting their impact on automation, design, and decision-making. A significant portion is dedicated to the burgeoning applications of LLMs in chemical engineering, including their use as educational tools, process simulation and modelling, reaction optimization, and molecular design. The review delves into specific case studies on distillation column and reactor design, showcasing how LLMs can assist in generating initial parameters and optimizing processes while also underscoring the necessity of validating their outputs against traditional methods. Finally, the review addresses the challenges and future considerations of integrating LLMs into engineering workflows, emphasizing the need for domain-specific adaptations, ethical guidelines, and robust validation frameworks. Full article

This study presents a comprehensive design, optimization, and intensification approach for enhancing the energy efficiency of electric grade isopropyl alcohol (IPA) production, a typical energy-intensive chemical process. The process entails preconcentration and dehydration steps, with the intensity of separation formulated from a multicomponent feed that consists of IPA and water, along with other impurities. Modeling and energy optimization were performed for a conventional distillation train as a base case by using the rigorous process simulator Aspen Plus V12.1. To improve energy efficiency, various options for intensifying distillation were examined. The side-stream preconcentration column was subsequently replaced by a dividing wall column (DWC) with two side streams, i.e., a Kaibel column, reducing the total energy consumption of corresponding distillation columns by 9.1% compared to the base case. Further strengthening was achieved by combining two columns in the preconcentration process into a single Kaibel column, resulting in a 22.8% reduction in reboiler duty compared to the base case. Optimization using the response surface methodology identified key operating parameters, such as side-draw positions and stage design, which significantly influence both energy efficiency and separation quality. The intensified Kaibel setup offers significant energy efficiencies and simplified column design, suggesting enormous potential for process intensification in energy-intensive distillation processes at the industrial level, including the IPA purification process. Full article

Background/Objectives: We developed and validated a robust and simple LC–MS/MS method for the simultaneous quantification of amoxicillin and clavulanate in human plasma relative to previously reported methods. Methods: Amoxicillin; clavulanate; and an internal standard, 4-hydroxytolbutamide, in human K₂-EDTA plasma, were deproteinized with acetonitrile and then subjected to back-extraction using distilled water–dichloromethane. Separation was performed on a Poroshell 120 EC-C₁₈ column with a mobile-phase gradient comprising 0.1% aqueous formic acid and acetonitrile at a flow rate of 0.5 mL/min within 6.5 min. The negative electrospray ionization modes were utilized to monitor the transitions of m/z 363.9→223.1 (amoxicillin), m/z 198.0→135.8 (clavulanate), and m/z 285.0→185.8 (4-hydroxytolbutamide). Results/Conclusions: Calibration curves exhibited linear ranges of 10–15,000 ng/mL for amoxicillin (r ≥ 0.9945) and 20–10,000 ng/mL for clavulanate (r ≥ 0.9959). Intra- and inter-day’s coefficients of variation, indicating the precision of the assay, were ≤7.08% for amoxicillin and ≤10.7% for clavulanate, and relative errors in accuracy ranged from −1.26% to 10.9% for amoxicillin and from −4.41% to 8.73% for clavulanate. All other validation results met regulatory criteria. Partial validation in lithium–heparin, sodium–heparin, and K₃-EDTA plasma confirmed applicability in multicenter or large-scale studies. This assay demonstrated itself to be environmentally friendly, as assessed by the Analytical GREEnness (AGREE) tool, and was successfully applied to a clinical pharmacokinetic study of an Augmentin^® IR tablet (250/125 mg). The inter-individual variabilities in clavulanate exposures (AUC_t and C_max) were significantly greater than in amoxicillin, and they may inform the clinical design of future drug–drug interaction. Full article

In recent decades, the attention of researchers has been directed towards the study of the dehydration of isopropanol (IPA) through different techniques. Besides its multiple uses in the chemical industry, IPA is also a potential bio-component in eco-friendly gasolines. Extractive distillation is a successful technique for separating IPA from a minimum boiling azeotrope with water. However, the major challenge is the production of fuel-grade IPA (minimum 99.92 mol%) with low expenses. As a consequent step in the investigation of IPA dehydration with propylene glycol as extractive solvent, the present study compares its efficiency and economic viability with two other extractive solvents, namely ethylene glycol (EG) and dimethyl sulfoxide (DMSO). A systematic and comprehensive methodology was developed to design a three-column extractive distillation (TCED) for each investigated solvent. A techno-economic assessment of all the investigated processes concluded that ethylene glycol, followed by propylene glycol, seems to be the most promising solvent in the IPA dehydration process. Further, the heat integration of hot streams (SH flowsheets) demonstrated improvements over 17% in the case of ethylene glycol solvent, around 16% in the case of propylene glycol (PG) solvent, and only 10% (in the case of DMSO solvent) reduction in utility consumption, improving the energy efficiency of TCED processes. Furthermore, SH flowsheets yield a 14% cost saving obtained in terms of total annualized cost (TAC) and, respectively, 8.69%, by comparison with TCED processes. In the case of DMSO solvent, the TAC reduction is only 3.54% due to the capital cost, which has an increase of 3% mainly due to the high solvent cost. Full article

High-purity distillation columns typically give rise to multi-variable, strongly coupled nonlinear systems with substantial time delay and significant inertia. The control performance of high-purity distillation columns crucially influences the purity of the final product. Taking into account the process of a high-purity distillation column, this article puts forward a dual-loop modified active disturbance rejection control (MADRC) scheme to improve the control of product purity. During the stable operation of the distillation process, the structures of two control loops are, respectively, approximated by two linear transfer function models via open-loop experiments. Subsequently, the compensation part of the MADRC scheme is designed, respectively, for each approximate model. Furthermore, this paper employs singular perturbation theory to prove the stability of MADRC. The performance of the dual-loop MADRC scheme (MADRC) is compared with that of a proportional–integral–derivative (PID) control scheme, a cascade PID control scheme (CPID), and a regular ADRC scheme (ADRC). The simulations demonstrate that the dual-loop MADRC scheme is capable of efficiently tracking the reference value and exhibits optimal disturbance rejection capabilities. Additionally, the superiority of the dual-loop MADRC scheme is validated through Monte Carlo trials. Full article

A Stir Bar Sorptive Extraction-Gas Chromatography-Mass Spectrometry (SBSE-GC-MS) method has been optimized and validated for the determination of eight volatile sulphur compounds in wine distillates: diethyl sulphide (DES), dimethyl disulphide (DMDS), diethyl disulphide (DEDS), 2-thiophenecarboxaldehyde (TC), dibutyl sulphide (DBS), dipropyl disulphide (DPDS), dipropyl sulphide (DPS), and dimethyl trisulphide (DMTS). After optimization by 2⁴ factorial design, the SBSE-GC-MS extraction conditions were as follows: a polydimethylsiloxane twister (10 mm × 0.5 mm), 35 °C as the extraction temperature, 10 mL as the sample volume, 7% (v/v) as the alcoholic grade, 47 min as the extraction time, 10% (w/v) of NaCl, and 1% (w/v) of EDTA (ethylenediaminetetraacetic acid). Under optimal conditions, adequate analytical figures of merit were obtained for eight of the ten compounds initially considered, with low quantification and detection limits and relative standard deviations for inter-twister and inter-day repeatability values ranging from 7.5 to 21.8% and from 7.2 to 27.5%, respectively. The methodology was applied to 34 wine distillates (continuous column distillation and distillation in pot still) elaborated for the production of Brandy de Jerez: 15 aged distillates aged for different periods of time in American oak wood and 19 non-aged distillates. The most significant volatile sulphur compounds were DBS, DMDS, and DPS. The Cluster Analysis (CA) on the volatile sulphur compounds grouped the samples according to the use of sulphur dioxide. In general, lower amounts of volatile sulphur compounds were found in the aged samples, although the high standard deviations obtained highlight that their contents depend on multiple factors related to the elaboration process. Full article

Amid the advancements in computer-based chemical process modeling and simulation packages used in commercial applications aimed at accelerating chemical process design and analysis, there are still certain tasks in design optimization, such as distillation column internals design, that become bottlenecks due to inherent limitations in such software packages. This work demonstrates the use of soft actor-critic (SAC) reinforcement learning (RL) in automating the task of determining the optimal design of trayed multistage distillation columns. The design environment was created using the AspenPlus^® software (version 12, Aspen Technology Inc., Bedford, Massachusetts, USA) with its RadFrac module for the required rigorous modeling of the column internals. The RL computational work was achieved by developing a Python package that allows interfacing with AspenPlus^® and by implementing in OpenAI’s Gymnasium module (version 1.0.0, OpenAI Inc., San Francisco, California, USA) the learning space for the state and action variables. The results evidently show that (1) SAC RL works as an automation approach for the design of distillation column internals, (2) the reward scheme in the SAC model significantly affects SAC performance, (3) column diameter is a significant constraint in achieving column internals design specifications in flooding, and (4) SAC hyperparameters have varying effects on SAC performance. SAC RL can be implemented as a one-shot learning model that can significantly improve the design of multistage distillation column internals by automating the optimization process. Full article

Heat-integrated distillation columns (HIDiC) are well known for their high energy efficiency, which has been demonstrated through thorough model-based simulation and practical testing. Despite this advantage, HIDiC systems are fundamentally complicated and provide major hurdles, particularly in terms of dynamic control, complicating their industrial implementation. Ongoing research is critical to improving their stability and scalability, allowing for wider incorporation into industrial processes. This review focuses on the fundamental aspects of HIDiC systems, such as heat transfer models, design improvements, experimental research, modelling, simulation, optimization, and process control techniques. This paper summarizes the present status of research and identifies significant technological obstacles that must be overcome to increase the functionality and industrial applications of HIDiC technology. In response to the increased demand for energy-efficient industrial processes, the analysis also investigates current developments in HIDiC control and optimization methodologies. It evaluates several control approaches, both model-based and data-driven, and their capacity to handle the dynamic complexities seen in HIDiC systems. Furthermore, this paper discusses the most recent optimization efforts targeted at improving product purity, operational flexibility, and overall energy efficiency. Full article

The extractive distillation process using propylene glycol (IUPAC name: 1,2 propanediol) as an extractive agent for the separation of the isopropanol–water system was investigated in this work. A systematic procedure was set out to obtain the optimal design and process conditions for extractive distillation and solvent recovery columns using the PRO/II process simulator. Four thermally integrated flowsheets were proposed, implying the recovery of the sensible heat and latent heat from the hot streams in the process. To establish the economic feasibility of the proposed process, we calculated the total annual cost for all the simulated versions, and the proposed fully thermally integrated flowsheets could save up to 43.13% in terms of the utility costs and up to 15.57% in terms of the TAC compared to the conventional design. Thus, propylene glycol (PG) is found to be suitable as a new solvent for isopropanol dehydration, being comparable with other classical solvents used for the dehydration of alcohols. Full article

This work proposes a conceptual design for recovering polystyrene (PS) using solvents of agro-industrial origin. The literature describes the dilution of expanded polystyrene (EPS) in limonene, followed by its insolubilization with alcohols for recovery. However, there is no information on the solubility limit for the PS + limonene + alcohol system, which is critical for the process design. To determine the solubility limit, we diluted the waste EPS in D-limonene, mixed it with ethanol to form a precipitate, and used a gravimetric method to measure the mass of the compounds. These results allowed for the conceptual design of an EPS recycling process using a chemical process simulator, which includes a separator, a distillation column, and auxiliary equipment such as heaters, coolers, and pumps. An empirical correlation was obtained for the solubility limit, which enabled the design of a process for the treatment of 52 kg/s of PS using 0.75 kg/s of ethanol and 2.4 kg/s of D-limonene once the stationary state had been reached. The distillation column is six-stage, with a reflux ratio of 1.5 and duties of 30,000 and −25 847 kW for the condenser and reboiler, respectively. Full article