Investigation of Fouling Effect on Overall Performance of Shell and Tube Heat Exchanger in a Urea Fertilizer Production Company in Nigeria

Sulaiman, M. A.; Kuye, S. I.; Owolabi, SA

doi:10.4314/njt.v35i1.20

Cited by 9 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The temperature gradient between the inlet (T si ) and outlet temperature (T so ) on the tube side indicates the operating efficiency of HE. The temperature gradient is compared with the minimal threshold (ΔT min s ) as in (2). Finally, the HE temperature gradient (ΔT HE Þ is evaluated as in (3) and constrained with the lower threshold (ΔT min HE ) to validate the predicted FR.…”

Section: Constrained Fr Estimationmentioning

confidence: 99%

“…Heat exchanger (HE) plays a vital role in regulating the temperature of products in process industries like sugar refineries, fertilizer manufacturing, petroleum, and oil refineries. [1][2][3] Shell and tube are one of the widely used HE, 4,5 which have fluids with different temperature gradients passing through the shell and tube enabling heat transfer. The suspended or dissolved solid particles in these fluids get deposited on the heat transfer surface, causing fouling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative quality weighted interpolation for LPV‐MPC control of industrial heat exchanger under varying fouling conditions

Jayalalitha

2022

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

Fouling is an inevitable phenomenon that tends to change the dynamics of the heat exchanger (HE). An accurate prediction model describing these varying HE dynamics is demanded to implement model predictive control (MPC), which has been addressed in this work. A novel iterative quality weighted interpolation (IQWI) technique is proposed to determine the prediction model from the linear parametric varying (LPV) model. It uses a set of linear state‐space (SS) models, which have been identified from the input–output data of the industrial HE under various fouling conditions. The quality of the identified models is assessed using a fuzzy inference system (FIS) based on fit percentage and data quality. IQWI technique uses incremental thresholds for the model quality to iteratively generate interpolation curves. Each interpolation curve is capable of estimating the model parameters, which are fused using the distance weighted method to determine the optimal model parameters. The proposed technique is capable of improving the model accuracy by 24.43%. The use of a fouling‐based prediction model equips the MPC with accurate knowledge of HE dynamics, which enables it to provide efficient control. Experimental analysis under five different fouling conditions and comparative analysis with the existing controller indicates the efficiency of the proposed method.

show abstract

Section: Constrained Fr Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative quality weighted interpolation for LPV‐MPC control of industrial heat exchanger under varying fouling conditions

Jayalalitha

2022

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

show abstract

“…It ensures heat transfer occurs between two fluids at different temperatures. Also, it can transmit energy in the form of latent heat, such as in condensers and boilers, or sensible heat, such as in coolers and heaters [4]. Heat exchangers are categorized based on the principal using criteria such as transfer processes, quantity of fluids, surface compactness, construction characteristics, heat transfer methods, and flow arrangements [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of fouling resistance prediction in shell and tube heat exchangers using advanced machine learning techniques

Ikram,

Djilali,

Abdennasser

et al. 2023

Res. Eng. Struct. Mat.

View full text Add to dashboard Cite

Heat exchangers are utilized in a vast region of the process industry for heating and cooling. Long-term operation of heat exchangers results in decreased efficiency due to many problems, such as fouling. Therefore, the object of this research paper is to use three artificial intelligence techniques (feedforward neural networks-multilayer perceptron (FNN-MLP), nonlinear autoregressive networks with exogenous inputs (NARX), and support vector machines (SVM-RBF)) for predicting the fouling resistance in the tube and the shell heat exchanger in the preheating circuit of atmospheric distillation. The results summarize the high training as well as the predictive capacity of the "FFNN-MLP" model for predicting the fouling resistance in the heat exchanger with the highest coefficient of correlation (R = 0.99961) and the lowest root-mean-squared error (nRMSE = 1.0031%) for the testing phase, where the FNN-MLP network is superior to that provided using the SVM model (R = 0.9955 and nRMSE = 3.8652%). All the models of artificial networks and machine learning techniques used in the current work can be used to predict the fouling resistance in heat exchanger data with high accuracy. Despite this, the FNN-MLP model is the preferred model compared with the other proposed models, followed by the NARX model.

show abstract

A Comprehensive Review on Fouling of Heat Transfer Units in Sugar Factory Ethanol Plant: Mechanisms and Mitigation Methods

Abo,

Hailegiorgis,

Gindaba

et al. 2024

Environmental Quality Mgmt

View full text Add to dashboard Cite

Fouling in heat transfer units has a negative economic impact. Many scale‐forming impurities are present in cane molasses generated by sugar cane production technology, including cations of aluminum (Al), calcium (Ca), iron (Fe), magnesium (Mg), potassium (K), sodium (Na), as well as anions of carbonates, sulfites, phosphates, sulfate, silicates, and chlorides. Ca cations, in particular, form insoluble complexes with many other chemical constituents, making them a scale‐forming impurity. The accumulation of Ca ion on the heat exchanger's surface could increase heat transfer resistance and reduce its overall efficiency. This article reviews many types of heat‐transmitting unit fouling and their successive fouling occurrences. Identifying the chemicals found in scale deposits helps to determine which cleaning products will effectively clean heat transfer units and which scale inhibitors will drastically lower scale formation rates. Furthermore, numerous unit operations and unit processes, such as molasses pre‐treatment and pre‐fermentation practice (inoculation of yeast), followed by fermentation practice, and product purification practice distillation, are used to limit deposit formation and boost ethanol production efficiency. Molasses pre‐treatment and treatment such as chemical treatment, heat treatment, acid centrifugation, and mechanical treatment are critical in decreasing scale development during heat exchange operations.

show abstract

Investigation of Fouling Effect on Overall Performance of Shell and Tube Heat Exchanger in a Urea Fertilizer Production Company in Nigeria

Cited by 9 publications

References 7 publications

Iterative quality weighted interpolation for LPV‐MPC control of industrial heat exchanger under varying fouling conditions

Iterative quality weighted interpolation for LPV‐MPC control of industrial heat exchanger under varying fouling conditions

Comparative analysis of fouling resistance prediction in shell and tube heat exchangers using advanced machine learning techniques

A Comprehensive Review on Fouling of Heat Transfer Units in Sugar Factory Ethanol Plant: Mechanisms and Mitigation Methods

Contact Info

Product

Resources

About