A Fuzzy Logic method: Predicting pipeline external corrosion rate

Biezma, M. V.; Saiz, Diego Agudo; Barron, Gonzalo

doi:10.1016/j.ijpvp.2018.05.001

Cited by 57 publications

(16 citation statements)

References 13 publications

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“…They can use inference of Mamdani type [31], of Takagi-Sugeno-Kang type [32] or be of relational type [33]. In order to solve the issue under consideration the fuzzy models with use of the Mamdani type inference are chosen because thanks to their generalized structure the accurate knowledge about the structure of the modeled object is not required [34]. Moreover, the inference rules of the models are not uniformly distributed along the solution space.…”

Section: Structure and The Operation Of The Model Of The Operational mentioning

confidence: 99%

Fuzzy Model of the Operational Potential Consumption Process of a Complex Technical System

Pająk

2020

FU Mech Eng

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During the operation process of a system its technical state is changed. The changes take place because of the wearing factors impact. The impact depends on the flow and intensity of the operation process what is characterized by the time histories of the working parameters. Simultaneously, the changes of the technical state are correlated with the changes of the amount of the operational potential included in a system. In order to avoid the inability state occurrence the amount of this potential should be higher than the boundary value. The amount of the operational potential included in a system is determined by the values of the cardinal features of it but in the case of the real technical system the values cannot always be measured. Therefore, the amount of the operational potential and the technical state of the system cannot always be determined online. To solve this problem the model of the operational potential consumption process was created and presented in the paper. The model uses artificial intelligence techniques to calculate the change of the operational potential amount by determining the changes of the cardinal features of the system on the basis of the time histories of the working parameters. The verification of the model quality was performed using the pulverized boiler OP-650k-040 operating in the power plant. The description of the conducted research and the results of the verification were presented in the end of the paper proving the adequacy of the model implementation in the case of industrial objects.

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Section: Structure and The Operation Of The Model Of The Operational mentioning

confidence: 99%

Fuzzy Model of the Operational Potential Consumption Process of a Complex Technical System

Pająk

2020

FU Mech Eng

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“…In this study, only the inductive form is introduced because the pipeline is buried at depth in the soil. This form occurs when AC flowing in the transmission line produces a magnetic [7] estimation of coating defects Roya and Bhardwajb [10] estimation of the pitting corrosion Yuji et al (2002) [8] FLC predicting pipeline external corrosion rate' Roya and Bhardwajb (2018) [10] and Ahmed (2018) [11] estimation of corrosion failure Kim and Seok (2018) [13], and Qi and Wei (2016) [16] pipeline risk assessment Victoria et al (2018) [15] estimation of failure probability Priyanka et al [18] adjust the flow rate of the petroleum products Aghay et al (2019) [9] ANN optimising anode location in an ICCP system Biezma and Agudo (2018) [12] studying inductive coupling between overhead power lines and nearby metallic pipelines Dipak and Barnali (2017) [14] they are predicting the condition of offshore oil and gas pipelines. Pavanaditya et al (2018) [17], and Priyanka et al (2018) [18] studying the electromagnetic interference Levente et al (2016) [19] and Wu (2015) [20] prediction of voltages on mitigated pipelines Xiaobin et al [27] estimation of the corrosion defect depth field around the transmission lines, which induced voltages on the nearby buried pipeline.…”

Section: Mechanisms Of Induced Ac Voltagementioning

confidence: 99%

“…A methodology was introduced to predict the buried pipeline's external corrosion rate based on the combination of the soil parameters. This method provided the inspection corrosion rate data and also focused the efforts towards optimising the security and the continuity of the service [12]. In [13], the impressed current cathodic protection (ICCP) anode locations on ships were selected based on minimising the underwater electric field.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of electrical hazards on the metallic pipelines due to the HVOHTLs based on different intelligent controllers

Al‐Gabalawy

Mostafa

Hamza

2020

IET Science, Measurement &Amp; Technology

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Many catastrophes may occur due to the interference between the metallic pipeline and the high voltage overhead transmission lines (HVOHTLs). These problems exist due to the neighbouring between the HVOHTLs and the other infrastructures. Many types of research cover cathodic protection points, detailed description for the production of the AC voltage, and preliminary mitigation ways. Mainly potassium hydroxide-polarisation cells (KOH-PCs) have been applied to discharge this voltage to the soil. Based on the KOH-PCs electrical model introduced previously, this study investigates a novel mitigation technique, where the discharged AC voltage converts to DC voltage that is proper for the cathodic protection for the metallic pipelines. An integrated system consists of power electronic circuits and distributed intelligent controllers designed to discharge the AC induced voltage and use in the cathodic protection for the pipelines. Different controllers such as the artificial neural network, a fuzzy logic controller, and adaptive neuro-fuzzy inference system controllers have been implemented. Two key performance indicators have been investigated to show the superiority of these controllers, where the total discharged energy per annum DE year from the pipeline to the soil, and the total saved per year SE year .

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“…This research shows that the model that was developed corresponds with the prediction on the rate of corrosion development with the soil pH and the resistivity of soil, both are set as an independent variable. A few research about the underground pipe and other structure corrosion has been done (Biezma et al, 2018;Wasim et al, 2018;Wang et al, 2018;Atanov et al, 2018;Thalib et al, 2018;He et al, 2012;Velayudhan and Yameni, 2012).…”

Section: Introductionmentioning

confidence: 99%

Expecting of Corrosion Rate in a Material Affected by Differences Soil Type in Controlled Environments.

Putra¹,

Muhammad²,

Huzni³

et al. 2020

Mate.corr.eng.manag.

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Research has been done on corrosion rates growth in SA 283 material which is influenced by differences in soil types in controlled environments. A283 grade D materials which are carbon steel formed into pipes with a diameter of 30″ which is used to distribute raw water for industrial needs. Weight loss methods that are in accordance with the ASTM G 162 standard have been used in this study. The buried time of the test specimen is 720 hours as one of the variables in calculating the value of the corrosion rate that occurs. The study was conducted at the Laboratory by controlling the soil structure, reducing potential, moisture level, soil pH level so that it did not change. The results of the study on soil types with a potential redox value of 60.6 mV with a pH level of 5.67 showed the greatest corrosion rate in material A283 that is 19.67 MPY or equal to 0.499 mm/yr. The results of this study concluded that the type of soil and its environment greatly affect the corrosion rate of carbon steel, so special attention is needed in protecting the pipe from corrosion attack.

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A Fuzzy Logic method: Predicting pipeline external corrosion rate

Cited by 57 publications

References 13 publications

Fuzzy Model of the Operational Potential Consumption Process of a Complex Technical System

Fuzzy Model of the Operational Potential Consumption Process of a Complex Technical System

Mitigation of electrical hazards on the metallic pipelines due to the HVOHTLs based on different intelligent controllers

Expecting of Corrosion Rate in a Material Affected by Differences Soil Type in Controlled Environments.

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