Optimization of refinery preheat trains undergoing fouling: control, cleaning scheduling, retrofit and their integration

Crude refining is one of the most energy intensive industrial operations. The large amounts of crude processed, various sources of inefficiencies and tight profit margins promote improving energy recovery. The preheat train, a large heat exchanger network, partially recovers the energy of distillation products to heat the crude, but it suffers of the deposition of material over time – fouling – deteriorating its performance. This increases the operating cost, fuel consumption, carbon emissions and may reduce the production rate of the refinery. Fouling mitigation in the preheat train is essential for a profitable long term operation of the refinery. It aims to increase energy savings, and to reduce operating costs and carbon emissions. Current alternatives to mitigate fouling are based on heuristic approaches that oversimplify the representation of the phenomena and ignore many important interactions in the system, hence they fail to fully achieve the potential energy savings. On the other hand, predictive first principle models and mathematical programming offer a comprehensive way to mitigate fouling and optimize the performance of preheat trains overcoming previous limitations. In this thesis, a novel modelling and optimization framework for heat exchanger networks under fouling is proposed, and it is based on fundamental principles. The models developed were validated against plant data and other benchmark models, and they can predict with confidence the main effect of operating variables on the hydraulic and thermal performance of the exchangers and those of the network. The optimization of the preheat train, an MINLP problem, aims to minimize the operating cost by: 1) dynamic flow distribution control, 2) cleaning scheduling and 3) network retrofit. The framework developed allows considering these decisions individually or simultaneously, although it is demonstrated that an integrated approach exploits the synergies among decision levels and can reduce further the operating cost. An efficient formulation of the model disjunctions and time representation are developed for this optimization problem, as well as efficient solution strategies. To handle the combinatorial nature of the problem and the many binary decisions, a reformulation using complementarity constraints is proposed. Various realistic case studies are used to demonstrate the general applicability and benefits of the modelling and optimization framework. This is the first time that first principle predictive models are used to optimize various types of decisions simultaneously in industrial size heat exchanger networks. The optimization framework developed is taken further to an online application in a feedback loop. A multi-loop NMPC approach is designed to optimize the flow distribution and cleaning scheduling of preheat trains over two different time scales. Within this approach, dynamic parameter estimation problems are solved at frequent intervals to update the model parameters and cope with variability and uncertainty, while predictive first principle models are used to optimize the performance of the network over a future horizon. Applying this multi-loop optimization approach to a case study of a real refinery demonstrates the importance of considering process variability on deciding about optimal fouling mitigation approaches. Uncertainty and variability have been ignored in all previous model based fouling mitigation strategies, and this novel multi-loop NMPC approach offers a solution to it so that the economic savings are enhanced. In conclusion, the models and optimization algorithms developed in this thesis have the potential to reduce the operating cost and carbon emission of refining operations by mitigating fouling. They are based on accurate models and deterministic optimization that overcome the limitations of previous applications such as poor predictability, ignoring variability and dynamics, ignoring interactions in the system, and using inappropriate tools for decision making.Open Acces

Twenty Years of Ebert and Panchal—What Next?

Ebert and Panchal introduced the “threshold fouling” approach for describing the initial rate of crude oil chemical reaction fouling at the meeting in this series of conferences held in San Luis Obispo, CA, in 1995. This paper summarizes reviews of developments in the threshold modeling approach over the last 10 years, following the review by Wilson et al. at the 2005 meeting. Three areas are considered: (i) The development of quantitative models, which has seen little activity but a switch toward using the threshold models to describe fouling dynamics. One of the reasons for the stagnation in development is the need to incorporate chemical understanding. (ii) The types and range of data sets that have been processed with these models, and an evaluation of the parameters. (iii) Applications where the models are used to predict fouling, or the likelihood of fouling. This is the area that has seen greatest activity, linked to the use of threshold models to describe fouling dynamics. Topics for future research and development are discussed

HEAT EXCHANGER NETWORK RETROFITTING

Ph.DDOCTOR OF PHILOSOPH

Framework for operability assessment of production facilities: an application to a primary unit of a crude oil refinery

This work focuses on the development of a methodology for the optimization, control and operability of both existing and new production facilities through an integrated environment of different technologies like process simulation, optimization and control systems. Such an integrated environment not only creates opportunities for op¬erational decision making but also serves as training tool for the novice engineers. It enables them to apply engineering expertise to solve challenges unique to the process industries in a safe and virtual environment and also assist them to get familiarize with the existing control systems and to understand the fundamentals of the plant operation. The model-based methodology proposed in this work, starts with the implementation of first principle models for the process units on consideration. The process model is the core of the methodology. The state of art simulation technologies have been used to model the plant for both steady state and dynamic state conditions. The models are validated against the plant operating data to evaluate the reliability of the models. Then it is followed by rigorously posing a multi-optimization problem. In addition to the basic economic variables such as raw materials and operating costs, the so-called “triple-bottom-line” variables related with sustainable and environmental costs are incorporated into the objective function. The methodologies of Life Cycle Assessment (LCA) and Environmental Damage Assessment (EDA) are applied within the optimization problem. Subsequently the controllability of the plant for the optimum state of conditions is evaluated using the dynamic state simulations. Advanced supervisory control strategies like the Model Predictive Control (MPC) are also implemented above the basic regulatory control. Finally, the methodology is extended further to develop training simulator by integrating the simulation case study to the existing Distributed Control System (DCS). To demonstrate the effectiveness of the proposed methodology, an industrial case study of the primary unit of the crude oil refinery and a laboratory scale packed distillation unit is thoroughly investigated. The presented methodology is a promising approach for the operability study and optimization of production facilities and can be extended further for an intelligent and fully-supportable decision making

Multi–scale Modelling of Refinery Pre–heat Trains Undergoing Fouling for Improved Energy Efficiency

Fouling in pre–heat trains of refinery crude distillation units causes major energy inefficiencies, resulting in increased costs, greenhouse gas emissions, maintenance efforts and health and safety hazards. Although chemical and physical phenomena underlying fouling deposition are extremely complex and several details remain unknown, the understanding of the fouling process has progressed significantly in the past 40 years. However, this knowledge has so far not been exploited to effectively improve heat exchanger and heat exchanger network design and operation. As a result, old methodologies that neglect the local effects and dynamics of fouling, in favour of lumped, steady–state, heuristic models (e.g. using TEMA fouling factors) are still used. In this thesis a novel mathematical model for pre–heat trains undergoing crude oil fouling was developed, validated with plant data and used to propose mitigation strategies. The model is dynamic, distributed and considers simultaneously several scales of investigation. Key phenomena are captured at the tube level as a function of local conditions. These include the dependence of fouling rate on temperature and velocity, the variation of physical properties, the structural changes of the deposits over time (ageing) and the dynamics of surface roughness. The single tube model was then extended to describe a unit–scale heat exchanger geometry. This has been validated against plant data from four units in two refineries operated by major oil companies. The predicted outlet temperatures over extended periods (i.e. 4-16 months) are accurate within ±1% for the tube–side and ± 2% for the shell–side. Model simulations were then used to assist the retrofit of one particular unit for which it was possible to save ca. 22% of the energy losses (not including pumping power) produced by fouling over ca. a year of operation. Finally, the interconnection of single heat exchangers in a network allowed the simulation of the fouling behaviour of two existing pre–heat trains. To systematically assess the impact of fouling on refinery economics, a set of key performance indicators (KPIs) was proposed. Network–level simulations were used in conjunction with the KPIs to unveil complex interactions and propose network retrofit arrangements that improve energy recovery over time whilst reducing fouling. It is concluded that the model can be used with confidence to predict fouling and assist monitoring, design and retrofit of refinery heat exchangers and heat exchanger networks. The results shown indicate that the approach proposed can lead to substantial benefits

MODELING, SIMULATION AND OPTIMIZATION OF INDUSTRIAL HEAT EXCHANGER NETWORK FOR OPTIMAL CLEANING SCHEDULE

Sustaining the thermal and hydraulic performances of heat exchanger network (HEN) for crude oil preheating is one of the major concerns in refining industry. Virtually, the overall economy of the refineries revolves around the performance of crude preheat train (CPT). Fouling in the heat exchangers deteriorates the thermal performance of the CPT leading to an increase in energy consumption and hence giving rise to economic losses. Normally the energy consumption is compensated by additional fuel gas in the fired heater. Thus, increase of energy consumption causes an increase in carbon dioxide emission and contributes to green house effect. Due to these factors, heat exchanger cleaning is performed on a regular basis either by chemical or mechanical cleanings. The disadvantage of these cleanings is the potential environmental problem through the application, handling, storage and disposal of cleaning effluents. Nevertheless, the loss of production caused by plant downtime for cleaning is often more significant than the cost of cleaning itself, particularly in refineries. Thus, it is essential to optimize the cleaning schedule of heat exchangers in the HEN of CPT. The present research focuses on the analysis of the effects of fouling on heat transfer performance and optimization of the cleaning schedule for the CPT. The study involves collection and analysis of plant historical operating data from a Malaysian refinery processing sweet crude oils. A simulation model of the CPT comprising 7 shell and tube heat exchangers post desalter with different mechanical designs and physical arrangements was developed under Petro-SIM™ environment to perform the studies. In the analysis of effects of fouling on heat transfer performance in CPT, the simulation model was integrated with threshold fouling models that are unique to each heat exchanger. The fouling model parameters are estimated from the historical data. The simulation study was performed for 300 days and the analysis indicated that the position of heat exchangers has a dominant role in the heat transfer performance of CPT under fouled conditions. It is observed from this simulation study, fouling of upstream heat exchangers of the CPT will have higher impact to overall heat transfer performance of the CPT. For the downstream heat exchangers, the decline in their heat transfer performances due to fouling can be compensated by the log-mean temperature difference (LMTD) effect, which will reduce or even increase the heat transfer performance of these heat exchangers. An optimization problem for the cleaning schedule of the CPT was formulated and solved. The optimization problem considered un-recovered energy cost and cleaning cost of the heat exchangers in the objective function. Optimization of the cleaning schedule was illustrated with a case study of simulation over a period of two years. Constant fouling rates that are extracted from the historical data are used to estimate the fouling characteristics of each heat exchanger in the CPT. For the purpose of comparison, a base case was developed based on the assumption that the heat exchangers will be cleaned when the maximum allowable fouling resistance was reached. mixed integer programming approach was used to optimize the cleaning schedule of heat exchangers. An optimized cleaning schedule with significant cost savings has been determined and reported over a period of two years