Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Petroleum refinery scheduling with consideration for uncertainty

Scheduling refinery operation promises a big cut in logistics cost, maximizes efficiency, organizes allocation of material and resources, and ensures that production meets targets set by planning team. Obtaining accurate and reliable schedules for execution in refinery plants under different scenarios has been a serious challenge. This research was undertaken with the aim to develop robust methodologies and solution procedures to address refinery scheduling problems with uncertainties in process parameters. The research goal was achieved by first developing a methodology for short-term crude oil unloading and transfer, as an extension to a scheduling model reported by Lee et al. (1996). The extended model considers real life technical issues not captured in the original model and has shown to be more reliable through case studies. Uncertainties due to disruptive events and low inventory at the end of scheduling horizon were addressed. With the extended model, crude oil scheduling problem was formulated under receding horizon control framework to address demand uncertainty. This work proposed a strategy called fixed end horizon whose efficiency in terms of performance was investigated and found out to be better in comparison with an existing approach. In the main refinery production area, a novel scheduling model was developed. A large scale refinery problem was used as a case study to test the model with scheduling horizon discretized into a number of time periods of variable length. An equivalent formulation with equal interval lengths was also presented and compared with the variable length formulation. The results obtained clearly show the advantage of using variable timing. A methodology under self-optimizing control (SOC) framework was then developed to address uncertainty in problems involving mixed integer formulation. Through case study and scenarios, the approach has proven to be efficient in dealing with uncertainty in crude oil composition

Prediction the individual component distillation curves of the blended feed using a hybrid GDM-PcLE method

A comprehensive knowledge of the properties and characterisations of the individual component in the blended feed is primary importance because different feedstock blending yields different products palate. Crude oil / condensate distillation unit optimization is an uphill task because unavailability of cheaper and reliable on line feed and product analyzers. Furthermore, laboratory analysis for feedstock characterization is very costly and time consuming. Alternatively, feed synthesis technique is used to reconcile the entire range of feed distillation curves by back blending the product streams from the actual column operation. The TBP and SG correlation are widely been used to estimate other bulk properties because they give the most accurate results. Due to highly nonlinear behaviour, methods like linear regression, non linear regression and rigorous models are adopted to predict TBP and SG distillation curves. The latter could give better accuracy results, but it is more complex, lengthy and costly to be implemented. In addition, the rigorous model commercially available such as PetrosimTM and Hysis 3.1TM are only being used to predict blended feed distillation curves, not for the individual component. Thus, a hybrid approach is proposed to overcome the deficiency of current methods and practices. The proposed method integrates the most versatile General Distribution Model (GDM) with a Pseudo-component Linear Equation (PcLE) method to predict the entire range individual component TBP and SG distillation curves of the blended feed from the readily available plant data, which are routinely taken by refiners. The predicted results given by hybrid GDM-PcLE model are almost agreeable with the lab results. A case study using the proposed short cut feed synthesis procedure and hybrid GDM-PcLE model showed additional 5% Naphtha yield can be achieved by changing the current feed blending ratio and product cut points. The accuracy of the predicting results can be improved if the distillates samples are to be carried out simultaneously and the flow meters are calibrated and corrected the measurements to density and temperature of the measuring devices. Since PcLE method is simple and open application, it can be easily integrated with iCONTM to enhance its application predicting the pure component TBP and other distillation curves from blended feed

INTEGRATED COMPUTER-AIDED DESIGN, EXPERIMENTATION, AND OPTIMIZATION APPROACH FOR PEROVSKITES AND PETROLEUM PACKAGING PROCESSES

According to the World Economic Forum report, the U.S. currently has an energy efficiency of just 30%, thus illustrating the potential scope and need for efficiency enhancement and waste minimization. In the U.S. energy sector, petroleum and solar energy are the two key pillars that have the potential to create research opportunities for transition to a cleaner, greener, and sustainable future. In this research endeavor, the focus is on two pivotal areas: (i) Computer-aided perovskite solar cell synthesis; and (ii) Optimization of flow processes through multiproduct petroleum pipelines. In the area of perovskite synthesis, the emphasis is on the enhancement of structural stability, lower costs, and sustainability. Utilizing modeling and optimization methods for computer-aided molecular design (CAMD), efficient, sustainable, less toxic, and economically viable alternatives to conventional lead-based perovskites are obtained. In the second area of optimization of flow processes through multiproduct petroleum pipelines, an actual industrial-scale operation for packaging multiple lube-oil blends is studied. Through an integrated approach of experimental characterization, process design, procedural improvements, testing protocols, control mechanisms, mathematical modeling, and optimization, the limitations of traditional packaging operations are identified, and innovative operational paradigms and strategies are developed by incorporating methods from process systems engineering and data-driven approaches

PSecurity Specification Language for Distributed Health Information System (DiHIS)

The introduction of policy based management which to manage distributed, complex and numerous systems is widely accepted and used in various sectors. The policy creators create policies that suit best for their operations and management. Since there are numerous of policies, this research focuses on the security policies only which are appointed to the distributed system of health information system. In order to implement the security policies, we need a language that can represent the security policies for distributed health information system completely. From the literature review conducted, there are numerous of security languages have been introduced since two decades ago. Those languages carry their own approaches representing the security policy and some of them do not support the characteristics of distributed system. There is no security language to implement the security policy for distributed health information system. This thesis introduces and initiates a security language to implement security policies in distributed health information system called DiHIS. Adding to that, there are three existing security languages used for discussion and comparison with the proposed DiHIS security language. They are ASL, LaSCO and Ponder. DiHIS security language has shown that it is able to represent the Security Policy Model for Clinical Information System completely compares to those three security languages. This language also has an added value when it covers the Need To Know Policy which other security languages do not. Need To Know Policy is one of the crucial issues in the health sector. DiHIS security language has also been tested with the application domain in health information system. The strength of the language can be seen with the ability of DiHIS to represent the security policies in various connections between various organizations involved in distributed health information system

Modeling the effect of blending multiple components on gasoline properties

Global CO2 emissions reached a new historical maximum in 2018 and transportation sector contributed to one fourth of those emissions. Road transport industry has started moving towards more sustainable solutions, however, market penetration for electric vehicles (EV) is still too slow while regulation for biofuels has become stricter due to the risk of inflated food prices and skepticism regarding their sustainability. In spite of this, Europe has ambitious targets for the next 30 years and impending strict policies resulting from these goals will definitely increase the pressure on the oil sector to move towards cleaner practices and products. Although the use of biodiesel is quite extended and bioethanol is already used as a gasoline component, there are no alternative drop-in fuels compatible with spark ignition engines in the market yet. Alternative feedstock is widely available but its characteristics differ from those of crude oil, and lack of homogeneity and substantially lower availability complicate its integration in conventional refining processes. This work explores the possibility of implementing Machine Learning to develop predictive models for auto-ignition properties and to gain a better understanding of the blending behavior of the different molecules that conform commercial gasoline. Additionally, the methodology developed in this study aims to contribute to new characterization methods for conventional and renewable gasoline streams in a simpler, faster and more inexpensive way. To build the models included in this thesis, a palette with seven different compounds was chosen: n-heptane, iso-octane, 1-hexene, cyclopentane, toluene, ethanol and ETBE. A data set containing 243 different combinations of the species in the palette was collected from literature, together with their experimentally measured RON and/or MON. Linear Regression based on Ordinary Least Squares was used as the baseline to compare the performance of more complex algorithms, namely Nearest Neighbors, Support Vector Machines, Decision Trees and Random Forest. The best predictions were obtained with a Support Vector Regression algorithm using a non-linear kernel, able to reproduce synergistic and antagonistic interaction between the seven molecules in the samples

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Recent advances in the characterization of gaseous and liquid fuels by vibrational spectroscopy

Date of Acceptance: 20/04/2015 Acknowledgments The author would like to thank Thomas Seeger, Alfred Leipertz, Florian Zehentbauer, Stella Corsetti, David McGloin, and Kristina Noack for fruitful discussions over the past decade. Special thanks to Lynda Cromwell and Andrew Williamson for proofreading the manuscriptPeer reviewedPublisher PD