Industrial Distillation Process Research Articles

Extraction of essential oil from Salvia fruticosa using a newly developed microwave-assisted distillation system for industrial applications

In this study, an industrial-scale microwave-assisted distillation (MWAD) is designed by combining 12 magnetrons with 1 kW, and the essential oil of the S. fruticosa plant is obtained, as a novelty. Also, the effect of microwave (MW) power applied at different times and powers on essential oil yield (%) and energy consumption is experimentally investigated and the statistical analysis using experimental data is carried out using the Design Expert program. For each test, 5 kg of dried plants as an herb are utilized and placed in a 100-L steam boiler. When the results are examined, the proposed MWAD system reduces the distillation time by half compared to the conventional distillation (CD) system. In terms of energy consumption, there is a huge energy saving due to the time being reduced by half. Moreover, compared to the essential oil yield obtained in the CD system, there is an increase in yield (average value of 15%) in the MWAD system. According to the chemical analysis results, no negative effect of MW power on essential oil quality is observed. However, MW power affects the percentage changes of some terpene groups. As a result, the MWAD system can be safely used in industrial distillation processes as an alternative to CD systems due to its high performance and low energy consumption.

Adaptive time window convolutional neural networks concerning multiple operation modes with applications in energy efficiency predictions

Energy efficiency prediction models promote the efficient uses of energy and low consumptions of raw materials. The Convolutional neural network (CNN) is one of the most effective deep learning networks for complex process modeling. However, when applied to real industrial processes, the performance of the CNN would be restricted by the change of operating conditions, such as swings in feedstock qualities, different manufacturing strategies and variations in product specifications. A globally invariant model is unable to adapt the time-varying conditions. Therefore, we proposed a multiple operation modes adaptive time window convolutional neural network (MOM-ATWCNN). Here, a hierarchical clustering approach is suggested to determine the numbers and locations of the modes. Then, an optimal length of time window is selected to match with each mode accordingly. Lastly, the improved deep learning model is used to extract the varying features hidden in different modes. To verify the effectiveness, the proposed method is compared to several typical deep learning models by the data collected from a real industrial atmospheric and vacuum distillation process. The results show that the energy prediction accuracy of the MOM-ATWCNN is 6.5%, 2.9% and 10.2% higher than those of the traditional CNN, LSTM, BPNN, respectively. Furthermore, the proposed method exhibit its superiority regarding various performance indexes. The improvement of the algorithm is beneficial to the reduction of energy consumptions thus achieving economic goals.

Adaptation of the Structure and Parameters of Nonlinear Soft Sensors by the Example of an Industrial Reactive Distillation Process

Adaptation of the Structure and Parameters of Nonlinear Soft Sensors by the Example of an Industrial Reactive Distillation Process

Multi-Output Soft Sensor with a Multivariate Filter That Predicts Errors Applied to an Industrial Reactive Distillation Process

The paper deals with the problem of developing a multi-output soft sensor for the industrial reactive distillation process of methyl tert-butyl ether production. Unlike the existing soft sensor approaches, this paper proposes using a soft sensor with filters to predict model errors, which are then taken into account as corrections in the final predictions of outputs. The decomposition of the problem of optimal estimation of time delays is proposed for each input of the soft sensor. Using the proposed approach to predict the concentrations of methyl sec-butyl ether, methanol, and the sum of dimers and trimers of isobutylene in the output product in a reactive distillation column was shown to improve the results by 32%, 67%, and 9.5%, respectively.

A probabilistic model with spike-and-slab regularization for inferential fault detection and isolation of industrial processes

This article develops a Bayesian latent variable model for inferential fault detection and isolation using a spike-and-slab regularization technique. Different from conventional probabilistic models like probabilistic principal component analysis (PPCA), a mixture prior with spike-and-slab component is introduced to indicate whether the latent variables are sensitive to the abnormal state. The relevant information of latent variables is selected by the probability of assigning the effect to the slab component, while the influence of the non-informative ones is eliminated by the spike component. Furthermore, a Bayesian inference scheme based on the expectation-maximization framework is developed for the parameter learning procedure of the model. The fault detection and diagnosis method based on the inferential model is subsequently developed. The performance of the proposed method is illustrated by the benchmark Tennessee Eastman process and an application to an industrial methanol distillation process.

DTDR–ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models

Taking advantage of varying degrees of attention on specific features, attention-based long short-term memory (ALSTM) networks have made inroads into the industrial multivariate time series prediction sector recently. However, conventional ALSTM models usually employ static time-delays to constant select input/output pairings of multivariate data, which apparently ignores dynamics of transmission time between industrial process variables and degrades the prediction performance by incorrectly extracting process characteristics. In response to this problem, this paper proposes a novel approach to extracting dynamic time-delays to reconstruct (DTDR) multivariate data for an improved ALSTM prediction model. Therein, the temporal locations and spans of multivariate data are adaptively tailored to input/output pairings of the ALSTM network according to the dynamic time-delays. Specifically, the multivariate data can be accurately matched in temporal positions, and the data information in the original temporal spans with Status transfer time abnormal are replaced. Consequently, this prediction model not only appropriately utilizes dynamics between the predicting and correlated variables, but also makes better attentions on key features extracted from optimum data. Applied to industrial distillation and methanol production processes, the proposed method demonstrates the capability of significantly improving network training speeds as well as prediction accuracies in contrast to static time-delay based ALSTM and LSTM models, expecting even more applications.

Structured sequential Gaussian graphical models for monitoring time-varying process

This article introduces a structured and sequential Gaussian graphical approach for monitoring time-varying industrial systems. To address the presence of time-varying trends, the article utilizes a sequential update procedure for Gaussian graphical models that is based on a moving window approach. The implementation of this approach requires that the Gaussian graph models are constructed based on two additional regularization terms. For a time-varying graphical structure, the overall trend can be isolated by the first additional regularization term; while abnormal events can be timely captured using the second regularization term. Unlike conventional graphical networks that rely on other methods for fault detection, this article proposes a unified graphical framework for fault detection and isolation. In addition, the optimization problem of the Gaussian graphical model can be solved using the Alternative Direction Method of Multiplier (ADMM) algorithm, which has a linear convergence rate, making it easily applicable to large-scale systems. The performance of this graph-based technique is demonstrated by a simulation example and the application study to an industrial distillation process.

Ecotoxicity of a new biopesticide produced by Lavandula luisieri on non-target soil organisms from different trophic levels

Plant-based biopesticides have become an eco-friendly alternative to synthetic pesticides by reducing the undesired environmental impacts and side-effects on human health. However, their effects on the environment and especially on non-target organisms have been little studied.This study analyses the ecotoxicological effects of the extract of Lavandula luisieri on soil non-target organisms from different trophic levels: the earthworm Eisenia fetida, the plant Allium cepa and a natural-soil microbial community whose taxonomy was analysed through 16S rRNA gene sequencing. The extract tested is the hydrolate –product from a semi industrial steam distillation process- of a Spanish pre-domesticated variety of L. luisieri. This hydrolate has been recently shown to have bionematicide activity against the root-knot nematode Meloidogyne javanica. A previous study showed that the main components of the hydrolate are camphor and 2,3,4,4-Tetramethyl-5-methylidenecyclopent-2-en-1-one.Hydrolate caused acute toxicity (LC50 2.2% v/v) on A. cepa, while only a slight toxicity on E. fetida (LC50 > 0.4 mL/g). All the concentrations tested (from 1 to 100% v/v) caused a significant decrease in bacterial growth (LC50 9.8% v/v after 120 h of exposure). The physiological diversity of the community was also significantly altered, except in the case of the lowest concentration of hydrolate (1% v/v). The ability of soil microbial communities to use a variety of carbon sources increased for all substrates at the highest concentrations.These results show that both the plants and bacterial communities of the soil can be affected by the application of biopesticides based on these hydrolates, which highlights the need for a more detailed risk assessment during the development of plant-based products.

Design and economic evaluation of energy-saving industrial distillation processes for separating close-boiling cyclohexanone-cyclohexanol mixture

In this study, several heat-integrated distillation processes are compared to separate the close-boiling cyclohexanone (CHON)-cyclohexanol (CHXA) for improving energy efficiency. A 4-column scheme, including two light ends columns in DRP (distillation with recycle process), a CHON column and a CHXA column, is introduced as the prototype scheme. To make improvement, light and heavy split DRP – are compared for energy optimization upon the prototype. The light split DRP turns out to be superior and thus used for subsequent steps. Four heat-integrated schemes including double-effect distillation (DED), heat pump assisted distillation (HPAD), DED plus HPAD (DEHP) and double HPAD (DHPAD) are sequentially suggested and evaluated based on total annualized cost (TAC). The results show TAC reductions from the prototype of 32.2%, 23.6%, 29.2% and 10.8% for DED, HPAD, DEHP and DHPAD, respectively. The DED scheme requires the lowest TAC due to the 48.4% operating cost and 8% capital cost saving, whereas the DEHP scheme has the lowest 56.3% operating cost reduction with 11.1% more capital investment. The line of thinking in this work will benefit in other close-boiling systems for higher energetic efficiency and economic profit.

Computer simulation and experimental molecular distillation of olive pomace oil deodorizer distillate – A comparative study

Olive pomace oil deodorizer distillate, a good source of oleic acid, was distilled using molecular distillation. In the present study, temperatures during this process ranged from 110 °C to 190 °C, while pressures were 6.67 Pa, 66.7 Pa, and 667 Pa. Changes in fatty acid concentrations as well as yields of distillate and residual streams were recorded experimentally. The molecular distillation process was then simulated with two different methods to observe the differences in fatty acid composition between real and computational studies using a commercial process simulator. A flash separation vessel was chosen to demonstrate short-path evaporator. Maximum distillate yield was experimentally obtained as 78.52% at 190 °C and 6.67 Pa, with a split ratio of 3.65. The results obtained from use of the two simulation methods were in good correlation with the experimental data. Oleic acid concentrations from the two different simulation methods at 190 °C and 667 Pa were calculated as about 71%, while the experimental concentration was 73.79% at this condition. The use of a simulation procedure would be a guide for further industrial distillation processes.

Energy efficiency improvement of a bioethanol distillery, by replacing a rectifying column with a pervaporation unit

Abstract Separation methods for ethanol production are among the most energy consumption in chemical industry. The design of efficient process is part of challenges faced by process engineering. The aim of this work is to reduce the operating energy and cost of a Moroccan distillery process. The separation process of this distillery consists of a distillation, purification and rectifying column under vacuum. In this study, a hybrid distillation-pervaporation process was proposed by replacing the rectifying column, which requires more than half of the total energy of the process, with the pervaporation unit. The pervaporation unit was modeled and simulated using appropriate model. Afterwards, the design of the hybrid process has been performed coupling the obtained pervaporation model with a simulator of the separation columns. The performance of the hybrid distillation-pervaporation process has been evaluated by simulation. A reduction of about 64.5% can be obtained for the operating energy and cost compared to the industrial distillation process.

Automated weighted outlier detection technique for multivariate data

In the chemical and petrochemical industries, spectroscopy-based online analysers are becoming common for process monitoring and control applications. A significant challenge in using these analysers as part of process monitoring and control loops is the large amount of personnel time required for calibration and maintenance of models which involve decision inputs such as whether an observation is an outlier, the number of latent variables in a model, type of pre-processing and when a calibration model has to be updated. Since no one measure works well for all applications, supervision by the process data analyst is required which invariably involves some level of subjectivity. In this paper, we focus on the detection of multivariate outliers in a calibration set. We propose a method which combines multiple outlier detection techniques to identify a set of outlying observations without operator input.Apart from the overall methodology, this work introduces several novelties. The system uses partial least squares (PLS) instead of principal component analysis (PCA) which is normally used for detecting multivariate outliers. A simple modification to the Mahalanobis distance was also proposed which appears to be more sensitive to outliers than the conventional Mahalanobis distance. The methodology also introduces the concept of a desirability function to enable automatic decision making based on multiple statistical measures for outlier detection. The methodology is demonstrated using Raman spectroscopy data collected from an industrial distillation process.

Identification of Nonlinear Soft Sensor Models of Industrial Distillation Process under Uncertainty

The advancement of method of nonlinear soft sensor development using a nonparametric approach based on the example of industrial multicomponent distillation column is considered. The proposed procedure for soft-sensors evaluation is directed to overcome such difficulties in practice as process nonlinearity and uncertainty due to unknown model structure and measurement errors of output.

Optimization of Extraction of Bioactive Compounds from Different Types of Grape Pomace Produced at Wineries and Distilleries

Natural extracts obtained from grape pomace are particularly interesting, due to the substantial variety of valuable compounds present with health benefits, specifically phenolic compounds such as anthocyanins, trans-resveratrol, quercetin, and proanthocyanidins. The production of such extracts has been recognized as a profitable way to valorize grape byproducts, which are low-value and most abundant. First, the effect of the solvent on the extraction of bioactive compounds from grape pomace is studied. The selected solvents are water and ethanol, biocompatible and available in wineries and distilleries. Then, different types of grape pomace obtained along the various stages of current industrial winemaking and distillation processes are analyzed. As a result, the best stage of the winemaking and distillation processes for pomace valorization is identified, corresponding to the grape byproduct with the highest potential as source of bioactive compounds. These studies were performed with Vitis vinifera variety of Tempranillo grapes (same year, same vineyard). This work optimizes the production of natural extracts from (byproduct) grape pomace with recognized health benefits, to be used as high value nutraceuticals ingredients. The process proposed uses renewable and low-cost resources existent in wineries and distilleries. The select solvent extracting is a mixture of the biocompatible water and ethanol. The selected fermented grape pomace was chosen from different and comparable types of grape pomace obtained at current winemaking and distillation processes, to be used in extraction without any pretreatment.

Vapor–Liquid Equilibrium Data for 1-Methyl-2-Pyrrolidone + (1-Butanol or 1-Hexene or Water) Binary Mixtures

The unusual properties of 1-methyl-2-pyrrolidone or N-methyl-2-pyrrolidone (NMP) make it a solvent of interest in industrial extractive distillation processes. To augment available data, vapor–liquid equilibrium (VLE) measurements were made for NMP + 1-hexene at (313.15, 335.15, and 363.15) K, for NMP + water at (343.15, 363.15, and 380.15) K, and for NMP + 1-butanol at (10, 25, and 50) kPa using a dynamic recirculating apparatus. Limiting activity coefficients are reported for water and n-hexene as solutes in NMP. Activity coefficient minima were found for 1-butanol as solvent in NMP in the very dilute regions at (10 and 25) kPa. All systems were modeled satisfactorily with the nonrandom two-liquid (NRTL) equation for the Gibbs excess energy with a variable alpha parameter. Thermodynamic consistency testing of the isothermal data with the Van Ness (Van Ness, H. C. Pure Appl. Chem. 1995, 67, 859–872) direct test is discussed.

Evaluation of Nonlinear Inferential Models to Estimate the Products Quality of Industrial Distillation Process

The problem of improvement of existing approaches for evaluation of inferential models used in the advanced process control systems is considered. The method of finding the identifiability index limit, based on the physically-chemical essence of the industrial distillation process, for the inferential models containing nonlinear transformations of measured inputs is given. The problem of handling the narrow variability range of key inputs in the training sample is considered. The paper contains the example of evaluating the inferential models based on the proposed method applied to the nonlinear industrial distillation plant.

Singular reactive flash dynamics

Reactive flash is commonly used as a toy model for understanding the dynamics of industrial reactive distillation processes. Reactive distillation can display complex bifurcation phenomena, including multiplicities and Hopf oscillations. In principle, the simplicity of reactive flash might provide important insights for the design, operation and control of reactive distillation processes. Recent studies have shown that steady state multiplicity and Hopf phenomena can be present even in reactive flash, and such bifurcations are the product of the interaction of the vapor–liquid separation with the chemical transformation. This work uses a simple reactive flash model to show that reactive flash can display singular dynamics represented by spontaneous transitions from one-phase (liquid) to two-phase (liquid-phase) operating modes. Different steady state operating scenarios are described, including a globally stable flashing to unfeasible operation leading to emptiness of the liquid phase. The results are discussed in terms of potential implications for the operation and control of reactive distillation columns.

Study of the Fusel Oil Distillation Process

Fusel oil is a byproduct obtained from bioethanol distilleries, composed of a mixture of higher alcohols such as isoamyl alcohol, isobutyl alcohol, and others. This study aimed to evaluate the industrial distillation process of fusel oil to obtain isoamyl alcohol using the Aspen Plus simulator, considering fusel oil as a mixture of nine components. Fusel oil samples collected in Brazilian industrial mills were analyzed by gas chromatography. An investigation of phase equilibrium (vapor–liquid equilibrium and liquid–liquid equilibrium) was carried out for the components involved in this mixture. Three configurations for the fusel oil separation process were proposed. The best design with minimum total annual cost (TAC) resulted in a recovery of 99.53% of isoamyl alcohol of product containing the isomers isoamyl alcohol (0.818 w/w) and active amyl alcohol (0.178 w/w). Dynamic control of this configuration was investigated, and the results show that reasonable control performance can be achieved.

A nonequilibrium model for distillation processes

AbstractA general thermodynamic model is developed by combining organically the nonequilibrium thermodynamic analysis with the traditional METSH simulation of distillation processes. The model is capable of both simulating industrial distillation processes and analyzing energy consumptions of the processes. The benzene–toluene binary distillation is selected as the model system to test the theory, and the industrial‐scale tray column is studied in this work. The simulation results show that the tray structure and operation parameters have significantly influenced the entropy generation rate (EGR). It is also found that the development of vapor and liquid flow patterns on the industrial‐scale trays can enhance tray efficiency and result in evident reduction of the EGR. The model is suitable for tray design, column performance analysis, and energy consumption evaluation of distillation processes. © 2006 American Institute of Chemical Engineers AIChE J, 2006

Recursive partial least squares algorithms for monitoring complex industrial processes

The monitoring of processes that exhibit non-stationary and/or time varying behaviour is discussed in this paper. It is shown that the application of recursive partial least squares (RPLS) algorithms together with adaptive confidence limits can lead to a considerable reduction in the number of false alarms. The integration of these algorithms into the multivariate statistical process control (MSPC) framework is introduced and its extensions to multi-block approaches is discussed. Example studies are given with respect to a simulation of a fluid catalytic cracking unit and the analysis of data obtained from an industrial distillation process.