Gasoline Blending Process Research Articles

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries. However, the optimization model is complex and difficult to build, which is a typical mixed integer nonlinear programming (MINLP) problem. Considering the large scale of the MINLP model, in order to improve the efficiency of the solution, the mixed integer linear programming - nonlinear programming (MILP-NLP) strategy is used to solve the problem. This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model, and a relaxed MILP model is constructed on this basis. The particle swarm optimization algorithm with niche technology (NPSO) is proposed to optimize the solution, and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes, the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results, thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications. The optimization result of the MILP model provides a good initial point. By fixing the integer variables to the MILP-optimal value, the approximate MINLP optimal solution can be obtained through a NLP solution. The above solution strategy has been successfully applied to the actual gasoline production case of a refinery (3.5 million tons per year), and the results show that the strategy is effective and feasible. The optimization results based on the closed-loop scheduling optimization strategy have higher reliability. Compared with the standard particle swarm optimization algorithm, NPSO algorithm improves the optimization ability and efficiency to a certain extent, effectively reduces the blending cost while ensuring the convergence speed.

Molecular Reconstruction Method Based on NIR Spectroscopy for Reformates

With the increasing attention to environmental protection and strict national standards, the production of high-quality clean gasoline is more and more required in modern refineries. At present, the molecular-level online modeling of the gasoline blending process is rarely reported due to the lack of a proper characterization method. In this study, we have developed a molecular reconstruction method based on near-infrared (NIR) spectroscopy. We established a library that comprises an NIR spectrum and molecular composition of known reformates. We developed the sample selection algorithm that finds the sample with an NIR spectrum close to the target sample. The method-solving algorithm based on multiple linear regression was used to obtain the molecular composition of gasoline quickly. The method combines online measurement methods and molecular management technology to achieve online composition reconstruction. Seventy-five groups of reformates were collected to verify the feasibility of the method, and the results showed that the method predicted well.

Novel adaptive sample space expansion approach of NIR model for in-situ measurement of gasoline octane number in online gasoline blending processes

For real-time measurement of gasoline octane number in online gasoline blending processes by near infrared (NIR) spectroscopy, this paper presents an adaptive sample space expansion approach (ASSEA) to improve the real-time measurement accuracy and to shorten the time for establishing the model. To improve the adaptability of the model, a comprehensive index that combines the characteristics of the query samples and the distance between the spectra is adopted to select the guide spectra. A sample construction method is proposed to supplement the original sample set with insufficient sample size. The new spectra are constructed with the labeled spectra based on the guide spectra. Subsequently, the concentration information of the constructed spectra is obtained on the basis of multi-way partial least square method. The proposed ASSEA method is compared with six representative methods. The prediction performance of ASSEA method (RMSE is 0.2442, and R2 is 0.8767) is significantly better than other methods. And compared with the traditional PLS method, RMSE and R2 are reduced and increased by 33.4% and 45.9%, respectively. Therefore, its efficiency is validated through an industrial gasoline blending process.

Weighted incremental minimax probability machine-based method for quality prediction in gasoline blending process

Near-infrared (NIR) spectroscopy is frequently used to predict quality-relevant variables that are difficult to measure online. This technology can be applied by developing the NIR model in advance. Obtaining a high-accuracy NIR model is difficult using traditional modeling methods because process data inherently contain uncertainties and present strong non-Gaussian characteristics. Considering the difficulty in obtaining precise prediction results, biased estimation is important in producing qualified products when NIR spectroscopy is used in a feedback quality control system. The present work proposes a biased estimation model based on probabilistic representation to address the aforementioned issues. Additionally, a novel weighted incremental strategy with “just-in-time” learning is proposed to improve model adaptiveness. In this way, the NIR model could be established and maintained without imposing any distribution hypothesis on process data, and biased estimation could be obtained in the form of probability. The performance of the proposed method is demonstrated on an actual data set from a gasoline blending process.

Biased Minimax Probability Machine-Based Adaptive Regression for Online Analysis of Gasoline Property

Near-infrared (NIR) spectroscopy plays a critical role in online analysis of difficult-to-measure properties of petrochemicals. In industrial applications, a calibration model among NIR spectra and properties must be established. However, it is a challenge to obtain a precision NIR model in the majority of petrochemical processes since industrial data present strong non-Gaussian and uncertainty characteristics. To deal with these problems, in the present work a probabilistic regression modeling method based on a biased minimax probability machine (BMPM) is proposed, without assuming any specific distributions for the data, in this article. In addition, a multiple locally weighted updating approach with a new supervised similarity distance is introduced to cope with process changes. The greatest advantage of the proposed approach is that it has superior capability in dealing with uncertainties and variations. The effectiveness of the method is illustrated through its application in an actual gasoline blending process and a simulated fermentation process.

Active training sample selection and updating strategy for near-infrared model with an industrial application

Abstract Training sample selection is widely accepted as an important step in developing a near-infrared (NIR) spectroscopic model. For industrial applications, the initial training dataset is usually selected empirically. This process is time-consuming, and updating the structure of the modeling dataset online is difficult. Considering the static structure of the modeling dataset, the performance of the established NIR model could be degraded in the online process. To cope with this issue, an active training sample selection and updating strategy is proposed in this work. The advantage of the proposed approach is that it can select suitable modeling samples automatically according to the process information. Moreover, it can adjust model coefficients in a timely manner and avoid arbitrary updating effectively. The effectiveness of the proposed method is validated by applying the method to an industrial gasoline blending process.

SDAE-BP Based Octane Number Soft Sensor Using Near-infrared Spectroscopy in Gasoline Blending Process

As the most important properties in the gasoline blending process, octane number is difficult to be measured in real time. To address this problem, a novel deep learning based soft sensor strategy, by using the near-infrared (NIR) spectroscopy obtained in the gasoline blending process, is proposed. First, as a network structure with hidden layer as symmetry axis, input layer and output layer as symmetric, the denosing auto-encoder (DAE) realizes the advanced expression of input. Additionally, the stacked DAE (SDAE) is trained based on unlabeled NIR and the weights in each DAE is recorded. Then, the recorded weights are used as the initial parameters of back propagation (BP) with the reason that the SDAE trained initial weights can avoid local minimums and realizes accelerate convergence, and the soft sensor model is achieved with labeled NIR data. Finally, the achieved soft sensor model is used to estimate the real time octane number. The performance of the method is demonstrated through the NIR dataset of gasoline, which was collected from a real gasoline blending process. Compared with PCA-BP (the dimension of datasets of BP reduced by principal component analysis) soft sensor model, the prediction accuracy was improved from 86.4% of PCA-BP to 94.8%, and the training time decreased from 20.1 s to 16.9 s. Therefore, SDAE-BP is proposed as a novel method for rapid and efficient determination of octane number in the gasoline blending process.

COMPARATIVE ANALYSIS ON NONLINEAR MODELS FOR RON GASOLINE BLENDING USING NEURAL NETWORKS

The blending process always being a nonlinear process is difficult to modeling, since it may change significantly depending on the components and the process variables of each refinery. Different components can be blended depending on the existing stock, and the chemical characteristics of each component are changing dynamically, they all are blended until getting the expected specification in different properties required by the customer. One of the most relevant properties is the Octane, which is difficult to control in line (without the component storage). Since each refinery process is quite different, a generic gasoline blending model is not useful when a blending in line wants to be done in a specific process. A mathematical gasoline blending model is presented in this paper for a given process described in state space as a basic gasoline blending process description. The objective is to adjust the parameters allowing the blending gasoline model to describe a signal in its trajectory, representing in neural networks extreme learning machine method and also for nonlinear autoregressive-moving average (NARMA) in neural networks method, such that a comparative work be developed.

A novel adaptive algorithm with near-infrared spectroscopy and its application in online gasoline blending processes

Near-infrared (NIR) spectroscopy has been widely used to estimate product qualities. Although numerous studies on NIR modeling methods have been conducted, few papers have reported the online application of an NIR spectrometer in the gasoline blending process. This study presents a novel adaptive modeling method to establish an NIR model for the gasoline blending process. This method is based on the local learning and recursive modeling framework. Based on the framework, the proposed method can adjust the model structure from two aspects: (i) in sampling intervals, the model is updated with a local learning strategy, and the weights of the training samples can be gradually adjusted; and (ii) when new reference samples become available, the new data pairs are selected and added to the training data set based on an effective evaluation mechanism. The high performance of the proposed algorithm was demonstrated through a spectroscopic data set from a real gasoline blending process. The research octane number (RON), as the most important properties of gasoline, was estimated. Several modeling methods such as recursive partial least squares (RPLS), partial least squares (PLS), and locally weighted PLS were utilized for comparison. The results show that the proposed approach produce more accurate results than the traditional RPLS and locally weighted PLS algorithms.

Online updating of NIR model and its industrial application via adaptive wavelength selection and local regression strategy

Near-infrared (NIR) spectroscopy has been widely used to estimate product quality or other key variables. The conventional updating strategy for an NIR model is based on new available samples. However, during a sampling interval, the model structure remains unchanged. To address this problem, in this article, a novel local regression strategy is proposed that can be adjusted according to process changes through wavelength selection and local regression approaches. The main idea of the presented algorithm is that for each query sample, a relevant calibration sample-set is selected, then the wavelength structure is updated and a local model is established. The performance of the method is demonstrated through an NIR dataset of gasoline, which was collected from a real gasoline blending and optimal control process. Compared with traditional partial least squares (PLS), locally weighted partial least squares (LW-PLS), and several other updating strategies, the proposed method is more accurate.

Complex System for Gasoline Blending Maintenance

The main aim of the study is to increase the efficiency of gasoline blending process using developed complex modeling system, it provides counting feedstock changes in the refinery, influencing detonation characteristics of trade gasoline. Final products of modeling system are precise and gasoline blending recipes are economically optimal. The relevance of the study is caused by the need to increase production volumes of trade gasoline in refineries that meet gasoline quality standards. Mathematical modeling and application of automatic computer systems are most relevant solutions for optimization of technologically difficult stages of oil production. The study considers the modeling approach to the blending process from the position of non-additivity of gasoline octane numbers. Practical results: The module of gasoline detonation characteristics developed in Borland «Delphi 7» workspace in conjunction with the module of chromatographic data analysis «Unichrom» and optimal recipes development module are combined in the universal complex system for gasoline blending process maintenance. The system allows taking into account feedstock composition changes on purpose to vary the recipes of trade gasoline blending. The developed complex system analyzes refinery conditions and recommends the feedstock supply in the process and allows getting essential economic benefit for the refineries by reducing the quality reserve of trade products.

Gasoline Blending System Modelling Via Static and Dynamic Neural Networks

Gasoline blending is an important unit operation in the gasoline industry. A good model for the blending system is beneficial for supervision operation, prediction of gasoline qualities, and realizing of model-based optimal control. The gasoline blending process involves two types of properties: a static blending property and a dynamic property of blending tanks. As the blending cannot follow the ideal mixing rule in practice, we propose static and dynamic neural networks to approximate the two types of blending properties. Input-to-state stability approach is applied to access robust learning algorithms of the two neural networks. Numerical simulations axe provided to illustrate the neuro modelling approaches.

Gasoline Blending System Modelling via Static and Dynamic Neural Networks

AbstractGasoline blending is an important unit operation in the gasoline industry. A good model for the blending system is beneficial for supervision operation, prediction of gasoline qualities, and realizing of model-based optimal control. The gasoline blending process involves two types of properties: a static blending property and a dynamic property of blending tanks. As the blending cannot follow the ideal mixing rule in practice, we propose static and dynamic neural networks to approximate the two types of blending properties. Input-to-state stability approach is applied to access robust learning algorithms of the two neural networks. Numerical simulations axe provided to illustrate the neuro modelling approaches.

Gasoline Blend Optimisation as a Local Unit in Hierarchical System

The paper presents gasoline recipe optimisation problem. The model of gasoline blending process, necessary to calculate the most important quality measures of the product is described. It is shown, that application of the "Complex" optimization algorithm to find the best (from both technological and economical point of view) recipe gives good results. The proposed system makes use of prices of gasoline components, so it is possible to utilize it in hierarchical optimization system with price coordination. Necessary modifications are described.

Modeling of a Gasoline Blending Process with Artificial Neural Networks

Abstract In petrochemical refineries the process of blending finished gasoline from several intermediate products exhibits synergetic and antagonistic effects between the properties of interest (e.g. octane numbers, volatility, vapor pressure, etc.) of the blending components. This nonlinear blending behavior makes it difficult to model the process mathematically. That contributes to the fact, that even today the blending process is often manually controlled, despite the inherent enormous potential for rationalization and efficiency enhancement. Artificial neural networks offer an alternative for the use of physical process models, especially for such processes, that are either nonlinear or otherwise difficult to model. This paper discusses their application to the blending process, which results in a drastically improved static blending model, thus causing significant cost savings and encouraging the next steps towards automatic quality control (blend property control).

Reduction of variation in the gasoline blending process —A case study

In an attempt to reduce the release of gasoline vapors into the atmosphere, the U.S. Environmental Protection Agency (EPA) has placed restrictions on the volatility of commercial gasoline. In particular, they have set an upper specification limit on the gasoline's volatility as measured by the Reid vapor pressure (RVP). Exceeding the EPA's upper specification limit may result in a fine to the refiner. While there is no lower specification limit imposed by the EPA, lower RVP values require greater production costs. This article describes the efforts of one refiner to meet the EPA requirements with minimum economic disbenefit. Various statistical tools were used during this effort. The paper demonstrates a variety of successful SPC applications in the continuous process industry.