Economical Aspect of Heat Exchanger Cleaning Affected by Fouling

Fouling is the accumulation of foulant in a heat exchanger. Fouling increases the pressure drop and energy loss. Losses due to fouling in the distillation unit for crude oil reached US $ 4.2-10 billion per year in the United State. Fouling couldn’t be avoided, but it could be mitigation. One of the mitigation method is periodic cleaning of the heat exchanger. The time interval of a heat exchanger cleaning schedule is 1-36 months. Energy recovery, additional costs due to the performance of the pump and the cleaning cost are used as variables to determine saving. Results from this research showed that the heat exchanger is cleaned at 9 month is optimal cleaning schedule. Heat exchanger has savings about IDR 7.9 billion at 9 month. The amount is derived from the energy recovery about IDR 8.8 billion, reduced by a cleaning cost about IDR 0.2 billion and the advantage due to additional pumping cost about IDR 1.1 billion

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

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Heat Exchanger Network Optimization for Multiple Period Operations

In this paper an optimization model is presented for the synthesis of a heat exchanger network (HEN) for multiperiod operations. A literature very well-known stagewise superstructure is used, but isothermal mixing assumption is not made and a timesharing procedure is adopted. A MINLP problem is solved separately for each period of operation. The final multiperiod HEN is synthesized automatically considering the greatest areas and not fixing matches in each device in different periods, which avoids excessive heat exchange areas. Heat exchangers are designed to be feasible in practice, with a minimum acceptable area. Three literature problems were used to test the applicability of the proposed model. The objective function aims to minimize the total annualized cost (TAC). During implementation of the model, inconsistencies found in the literature were corrected. Results indicate that lower TACs were obtained in the present paper and each heat transfer device is feasible in practice.The authors acknowledge the support provided by CAPES (Coordination for the Improvement of Higher Education Personnel)−Brazilian Education Ministry

Operation optimisation study for CCGT power plant.

A major concern for the power generation industry is to obtain a maximum economic benefit without over-consuming the remaining life of the gas turbine hot section. This study explored a methodology to support decision making for operational optimisation of a combined cycle gas turbine (CCGT) power plant. There is no published algorithm for modelling a parallel dual pressure, once-through steam generator (OTSG), nor any proposed method for OTSG degradation diagnosis and how the degradation affects OTSG performance. What is more, few publications were found for optimisation existing power plant operation considering gas turbine creep life. This study presents a new thermodynamic algorithm to simulate the thermodynamic performance of parallel dual pressure OTSG. In this study, a novel gas path diagnostic method for an OTSG based on the Newton-Raphson method was developed to predict the OTSG degradation caused by fouling. A daily operation decision support platform for this existing power plant is proposed that models CCGT performance, creep life, emissions, economics, and provides a basis for decision-making based optimised results. The OTSG performance model is applied to an OTSG operating in a CCGT power plant at Manx Utilities on the Isle of Man, United Kingdom to demonstrate the effectiveness of the simulation method. A comparison between predicted OTSG performance and OTSG field data showed that the proposed model offers good prediction accuracy when simulating OTSG performance for both design and off-design points. The OTSG diagnostic system was applied to a model OTSG to test its effectiveness. The impact of measurement noise on the diagnostic accuracy was also analysed and discussed. A comparison between predicted and implanted degradation of a model OTSG demonstrated that the results were satisfactory, and the method is promising. Moreover, the diagnostic analysis of an OTSG based on real measurement has further proved that the proposed diagnostic method works well. This simulation will recommend to the plant operator optimal operation schedules taking into consideration thermo-economics and lifing, under conditions of variation of power demand, electricity price, ambient conditions and gas turbine engine health states. It will suggest the more severely degraded engine should run at a relatively lower power setting to decrease creep life consumption. The established power plant optimisation framework will assist power plant operators to decide the total power output and power split between generators based on an optimisation system that considers both immediate economic benefit and life considerations. It will help existing power plant to adjust daily operation to achieve better thermoeconomic and lifing benefits. The outcome of this research will be useful for industrial CCGT power generation.PhD in Aerospac