Nonlinear System Identification of Laboratory Heat Exchanger Using Artificial Neural Network Model

This paper addresses the nonlinear identification of liquid saturated steam heat exchanger (LSSHE) using artificial neural network model. Heat exchanger is a highly nonlinear and non-minimum phase process and often its working conditions are variable. Experimental data obtained from fluid outlet temperature measurement in laboratory environment is used as the output variable and the rate of change of fluid flow into the system as input too. The results of identification using neural network and conventional nonlinear models are compared together. The simulation results show that neural network model is more accurate and faster in comparison with conventional nonlinear models for a time series data because of the independence of the model assignment.DOI:http://dx.doi.org/10.11591/ijece.v3i1.195

Fouling Mitigation Suite: GUI Development in Excel

The objective of this project is to develop a fouling prediction model in crude preheat train (CPT) in refinery. Heat exchanger is a very important device of equipment that is commonly used in industry such as petrochemical, processing and refinery plant. Heat exchanger is used to transfer heat from a liquid on one side of a barrier to a fluid on the other side without bringing the fluid into direct contact. The crude preheat train (CPT) in a petroleum refinery consist consists of a setof large heat exchangers which recovers the waste heat from product streams to preheat the crude oil. The accumulation of unwanted deposits on heat transfer equipment results in reduced efficiency of heat recovery. This phenomenon is called heat exchanger fouling. In addition to this project, the research will be associated with the development of user interface (GUI) for reading and writing data into the model. Fouling model is developed based on the actual data taking from PETRONAS Penapisan Melaka (PPMSB). The model will predict the fouling resistance, Rfonce the input which are crude andproduct properties, crude and product mass flowrate, crude and product inlet temperature and time. Rf will be used in the simulation of heat exchanger using PETROSIM for the calculation of actual outlet temperature of crude and product. All the product and crude properties is generated in the PETROSIM before it was exported to the Microsoft Excel. The results which are the outlet temperature of the crude and product obtained in the model will be compared with the actual data from refinery plantafterthe reconsilation, to justifythe accurateness of the model

Genetic algorithm for the design and optimization of a shell and tube heat exchanger from a performance point of view

A new approach to optimize the design of a shell and tube heat exchanger (STHX) is developed via a genetic algorithm (GA) to get the optimal configuration from a performance point of view. The objective is to develop and test a model for optimizing the early design stage of the STHX and solve the design problem quickly. GA is implemented to maximize heat transfer rate while minimizing pressure drop. GA is applied to oil cooler type OKG 33/244, and the results are compared with the original data of the STHX. The simulation outcomes reveal that the STHX\u27s operating performance has been improved, indicating that GA can be successfully employed for the design optimization of STHX from a performance standpoint. A maximum increase in the effectiveness achieves 57% using GA, while the achieved minimum increase is 47%. Furthermore, the average effectiveness of the heat exchanger is 55%, and the number of transfer units (NTU) has improved from 0.475319 to 1.825664 by using GA

Optimum Design of Shell and Tube Heat Exchanger Using Artificial Immune System Approach

This paper presents the economic optimization of shell and tube heat exchangers design approach through an artificial immune system algorithm for minimizing the cost. Since complex geometric parameters, with thermodynamic and fluid dynamic factors, consume more time and offer a minimum possibility for an optimum result in the case of conventional design, the design process becomes difficult. The proposed algorithm provides the designer with an optimum solution in less amount of time by analyzing three different case studies.  Three design variables such as shell internal diameter, tube outer diameter and baffle spacing from the different design parameters are taken into account for this optimization. The results are weighed against those obtained by various researchers

Thermal Performance Analysis of Triple Heat Exchangers via the Application of an Innovative Simplified Methodology

Despite the double tube heat exchangers, in the triple tube heat exchangers, there are three fluids, and the methodology based on the assessment of the logarithmic mean temperature difference is no longer applicable. Moreover, in triple tube heat exchangers, there are two overall heat transfer coefficients dependent on each other. As such, it is necessary to solve them simultaneously, thus making the evaluation of the thermal performance of triple tube heat exchangers more complex compared to double tube heat exchangers. Among the proposed approaches in the literature to solve this issue, one of the most powerful and commonly adopted in several engineering applications is the parameter estimation procedure. Nevertheless, for the specific implementation examined in our analysis, a thorough numerical model of the triple tube heat exchanger was required to apply the inverse procedure properly. Furthermore, it is mandatory to measure the temperature of the three fluids at the inlet and outlet sections. In so doing, the inverse procedure can be successfully applied to the characterisation of triple tube heat exchangers tested in well-equipped research labs; however, its application to heat transfer devices operating in industrial facilities can be difficult. In order to overcome this limitation, an innovative parameter estimation technique that enables the evaluation of the thermal performance of this type of heat transfer devices is presented. The suggested methodology is based on a simple model of the triple tube heat exchanger in which an equivalent double tube heat exchanger is considered, thus requiring only four temperature measurements. The results obtained by applying this simplified methodology are numerically validated and compared to those obtained using a comprehensive mode

Comparison of daily rainfall forecasting using multilayer perceptron neural network model

Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values

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

An experimental study of clogging fault diagnosis in heat exchangers based on vibration signals

The water-circulating heat exchangers employed in petrochemical industrials have attracted great attentions in condition monitoring and fault diagnosis. In this paper, an approach based on vibration signals is proposed. By the proposed method, vibration signals are collected for different conditions through various high-precision wireless sensors mounted on the surface of the heat exchanger. Furthermore, by analyzing the characteristics of the vibration signals, a database of fault patterns is established, which therefore provides a scheme for conditional monitoring of the heat exchanger. An experimental platform is set up to evaluate the feasibility and effectiveness of the proposed approach, and support vector machine based on dimensionless parameters is developed for fault classification. The results have shown that the proposed method is efficient and has achieved a high accuracy for benchmarking vibration signals under both normal and faulty conditions