Advanced Process Control

The debutanizer column is an important unit operation in petroleum refining industries. The top product is liquefied petroleum gas and the bottom product is light naphtha. This system is difficult to handle. This is because due to its non-linear behavior, multivariable interaction and existence of numerous constraints on its manipulated variable. Neural network techniques have been increasingly used for a wide variety of applications. In this book, equation-based multi-input multi-output (MIMO) neural network has been proposed for multivariable control strategy to control the top and bottom temperatures of the column. The manipulated variables for column are reflux and reboiler flow rates, respectively. This neural network model are based on multivariable equation, instead of the normal black box structure. It has the advantage of being robust in nature while being easier to interpret in terms of its input-output variables. It has been employed for set point changes and disturbance changes. The results show that the neural network equation-based model for direct inverse and internal model approach performs better than the conventional proportional, integral and derivative (PID) controller

Inferential Development ofMLNG Depropanizer Bottom Product

This is an individual Final Year Project titled as 'Inferential Development for MLNG Depropanizer Bottom Product' which carries four credits hours. The main objective of this research project is to develop an appropriate inferential model to predict the quality of a Depropanizer bottom product that consists ofbutane and propane. In this research project, neural network technique was employed to predict the property of the Depropanizer bottom product. There were twenty seven inputs and one output used to develop the neural network model. This research project was carried out in conjunction with MLNG whereby data were collected from the plant to construct the network and training itto perform the property prediction. The software used for this project is Matlab 6.1 especially neural network toolbox and Microsoft Excel. The neural network used was of 'Feed Forward Backpropagation' type and suitable configuration was tested and analyzed to achieve a minimum number of prediction error. For this project, the error calculation used was Root Mean Square (RMS). The network model were developed with the configuration of 3 layers which consist of 36 neurons in the first layer, 27 neurons inthe second layer and 1neuron inthe third layer. The training function used for this network is 'Trainrp' and the adaptation learning function is 'Learngdm'. This network was trained with 100 times iteration. The model can be considered accurate to predict the concentration of the propane at the Depropanizer bottom product with RMSE obtained at 5.36%

Composition Prediction of Debutanizer Column using Neural Network

In oil refining industries, debutanizer column is one of the important unit operations. Debutanizer column is the main column used to produce the main product in oil refinery process. The online composition prediction of top and bottom product of debutanizer column using neural network will be an aid to increase product quality monitoring in oil refining industry. In this work, a single dynamic neural network model is used in order to achieve the objective which is to generate composition prediction online of the top and bottom product of debutanizer column. Neural network is a computing system with several of simple and highly interconnected processing elements that will process information using their dynamic state response to external inputs. It is a software based sensor method or known as “soft sensor” which is a helpful technology that utilizes software techniques to infer the value of important but difficult-to-measure process variables from available process variables which are requisite from physical sensor observation or lab measurements. The neural network development and equation based model for ibutane, i-pentane, n-butane, n-pentane and propane has been obtained. Then, these results will be compared with proportional integral derivatives (PID) controller design to show its supremacy over this method

Monitoring and control for NGL recovery plant

The thesis explores the production of natural gas liquids (NGL) and the challenge of monitoring and controlling the fractionation process. NGLs are the C2+ hydrocarbon fraction contained in natural gas, which includes useful feedstocks for industrial production processes. Since NGLs have greater economic value compared to natural gas, their recovery has become increasingly economically significant, leading to a need for efficient fractionation. This energy-intensive process is typically conducted in separation trains that include cryogenic distillation columns. Given the high cost of composition analyzers and the related significant delays, this work proposes the use of only indirect composition control strategies, as well as data-driven control strategies to achieve the desired product quality and optimize the plant energy consumption under typical disturbances. Feedforward neural networks (FFNs) were used for the development of soft sensors used in data-driven control schemes. Given the multitude of data made available by the process simulator, this work aims to develop a demethanizer digital twin that can approximate the column dynamics with reduced computation time. Long Short-Term Memory neural networks (LSTM), along with physical knowledge, were used to develop different neural network architectures compared to select the most suitable for the surrogate model development. Realistic measurement noises were considered to accurately reflect the measurements of real industrial plants and only easy-to-measure variables were used as input data for the developed neural model. Overall, the research presents an energy-efficient NGL recovery offering a cost-effective and efficient alternative to traditional measuring instruments. Moreover, the study illustrates a novel application of LSTM for distillation columns digital twins realization, providing a useful tool for optimization, monitoring and control by employing available plant measurements

Linear Model Predictive Control of a Debutanizer Column (Simulation Work)

This dissertation has been prepared in order to fulfill the partial requirement of a final year chemical engineering student in Universiti Teknologi PETRONAS (UTP). It is submitted to the university as a requirement of the Final Year Project II. Linear model predictive control studies have been an interesting field of research in the scope of chemical engineering. This research has been done in order to benefit the operations of PETRONAS Penapisan Terengganu Sdn. Bhd., PP(T)SB. The C-110 Debutanizer column at the plant is not achieving its desired output which is 30% propane and 70% butane. Therefore this study is conducted in order to obtain the optimum tuning parameters for Model Predictive Control, MPC controllers that can help achieve the desired output at the plant operation. The C-110 Debutanizer column has been simulated using HYSYSTM software according to real plant data. By conducting an open loop step test, relevant data were collected in order to proceed to MATLAB programming. By using the IDENT System Identification tool available in MATLAB, simple programming and coding were done to obtain necessary input information for the MPC controllers. MPC controllers with different tuning settings were tested in the HYSYSTM environment and the best tuning method is suggested to PP(T)SB to enhance their plant operation. Chapter 1 provides a short introduction on the background of the project. The problem statements of this project will be well discussed in accordance to its objectives. Chapter 2 gives a detailed literature review on the mentioned topic of research. In this chapter, the concept and basic understanding of the project is shown. In Chapter 3, the research methodology and project activities are mentioned. The milestones of this project are also presented. Chapter 4 shows the results gained in this project. The relevancy of this project to its objectives and its probable future works are also discussed. Chapter 5 discusses the conclusion and recommendations for this project. The findings of this project will help the operations at PETRONAS Penapisan Terengganu Sdn. Bhd., PP(T)SB in order to achieve the desired output of the C-110 Debutanizer column

An Expert System to Improve the Energy Efficiency of the Reaction Zone of a Petrochemical Plant

Energy is the most important cost factor in the petrochemical industry. Thus, energy efficiency improvement is an important way to reduce these costs and to increase predictable earnings, especially in times of high energy price volatility. This work describes the development of an expert system for the improvement of this efficiency of the reaction zone of a petrochemical plant. This system has been developed after a data mining process of the variables registered in the plant. Besides, a kernel of neural networks has been embedded in the expert system. A graphical environment integrating the proposed system was developed in order to test the system. With the application of the expert system, the energy saving on the applied zone would have been about 20%.Junta de Andalucía TIC-570

Linear Model Predictive Control of a Debutanizer Column (Simulation Work)

This dissertation has been prepared in order to fulfill the partial requirement of a final year chemical engineering student in Universiti Teknologi PETRONAS (UTP). It is submitted to the university as a requirement of the Final Year Project II. Linear model predictive control studies have been an interesting field of research in the scope of chemical engineering. This research has been done in order to benefit the operations of PETRONAS Penapisan Terengganu Sdn. Bhd., PP(T)SB. The C-110 Debutanizer column at the plant is not achieving its desired output which is 30% propane and 70% butane. Therefore this study is conducted in order to obtain the optimum tuning parameters for Model Predictive Control, MPC controllers that can help achieve the desired output at the plant operation. The C-110 Debutanizer column has been simulated using HYSYSTM software according to real plant data. By conducting an open loop step test, relevant data were collected in order to proceed to MATLAB programming. By using the IDENT System Identification tool available in MATLAB, simple programming and coding were done to obtain necessary input information for the MPC controllers. MPC controllers with different tuning settings were tested in the HYSYSTM environment and the best tuning method is suggested to PP(T)SB to enhance their plant operation. Chapter 1 provides a short introduction on the background of the project. The problem statements of this project will be well discussed in accordance to its objectives. Chapter 2 gives a detailed literature review on the mentioned topic of research. In this chapter, the concept and basic understanding of the project is shown. In Chapter 3, the research methodology and project activities are mentioned. The milestones of this project are also presented. Chapter 4 shows the results gained in this project. The relevancy of this project to its objectives and its probable future works are also discussed. Chapter 5 discusses the conclusion and recommendations for this project. The findings of this project will help the operations at PETRONAS Penapisan Terengganu Sdn. Bhd., PP(T)SB in order to achieve the desired output of the C-110 Debutanizer column

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

In a pharmaceutical industry, batch extractive distillation (BED), a combination process between extraction and distillation processes, has been widely implemented to separate waste solvent mixture of acetone-methanol because of minimum-boiling azeotrope properties. Normally, water is used as solvent and semi-continues mode is proposed to improve purity of acetone. The solvent is charged into the BED column until the purity of a desired product is achieved. After the total reflux start-up period is ended, a dynamic optimization strategy is applied to determine an acetone distillate composition profile maximizing the weight of the distillate product (acetone). The acetone distillate composition profile is used as the set point of neural network model-based controllers: the neural network direct inverse model control (NNDIC) and neural network based model predictive control (NNMPC) in order to provide the acetone composition with the purity of 94.0% by mole within 9.5 hours. It has been found that although both NNDIC and proportional integral derivative (PID) control can maintain the distillate purity on its specification for the set point tracking and in presence of plant uncertainties, the NNMPC provides much more satisfactory control performance and gives the smoothest controller action without any fluctuation when compared to the NNDIC and PID

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column

Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-ofthe- art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.This research received no external funding