Effects of Electrode Types on the Microstructure, Tensile and Hardness Properties of 304 L Austenitic Stainless Steel Heat-Affected Zone (HAZ)

Submerged arc welding (SAW) has been well utilised for the production of weld joints in 304 L ASS for various industrial application. However, effective performance of the material in service has been hampered by improper choice of electrode. Therefore, in this study, effects of different types of electrode on the microstructure and tensile property of type 304 L austenitic stainless steel heat-affected zone (HAZ) were studied. Chemical composition of the as-received sample was determined. A number of samples were cut from the as-received sample. Afterwards, two half were joined together with 308 L, 312 L and 316 electrodes at a controlled welding speed, current and voltage of 4.6 mm/s, 160 A and 30 V to produce a constant heat input of 626.09 J/mm. An automatic SAW machine with Model Type: DX3-301, and Frequency: 50 Hz was used. And based on ASTM standard, tensile and hardness samples were prepared from the as-received and HAZs. Tensile and hardness measurements were made. Also, specimens for microscopy studies were prepared from the HAZ and as-received samples. From the results, microstructures of the HAZs revealed varied volume fraction of austenite and ferrite phases and grain sizes, and at austenite and ferrite grain boundaries, chromium carbide formation and precipitation were observed. The weld joint produced with 308 L electrode revealed optimum UTS value and YS value of 475 and 325 respectively. While weld joint produced with 316 L electrode has superior ductility of value 41%. Irrespective of the types of electrode used, the as-received sample revealed superior tensile properties over the weld joints. Also, optimum hardness value of 45.7 HRA was obtained with 308 L. Hardness value of the as-received sample was higher than that of HAZ samples.

Review of GTAW Welding Parameters

Welding as a fabrication process is one of the vital production routes for most manufacturing industries. Several factors are involved in the choice of welding process for specific applications;notable among these are compositional range of the material to be welded, the thickness of the base materials and type of current. Most metals oxidize rapidly in their molten state, and therefore, the weld area needs to be protected from atmospheric contamination;this is achieved in gas tungsten arc welding GTAW by a shielding gas (argon, helium, nitrogen). GTAW technique is one of the major processes for joining austenitic stainless steels (ASS) and ferritic stainless steel (FSS) fabrication. However, the microstructural change that occurs during welding and at weld joint is still a major challenge today as it affects both the corrosion resistance and the mechanical properties. Therefore, this present paper reviews past research findings on GTA welding of ASS and FSS. Results of the findings have confirmed that, depending on the amount of heat input, which can be controlled by welding parameters (welding speed, voltage and current), welded joints particularly, heat affected zones (HAZs) of both grades of steels can undergo mechanical failure and can be susceptible to corrosion attack if the joints are produced with a less ideal combination of welding parameters.

Environmental Impact on Surface and Ground Water Pollution from Mining Activities in Ikpeshi, Edo State, Nigeria

The study was carried out to evaluate the surface and groundwater condition from mining activities in Ikpeshi and its environs in Akoko Edo Local Government Area of Edo State, Nigeria. Twenty water samples were randomly collected and analyzed—one borehole water sample, two hands dug wells, eight river samples and nine quarry pits water samples. The physiochemical, heavy metal and bacteriological analysis of the water sample, as well as the variables were compared with those of the World Health Organization (WHO) standard (2008), United State Environmental Protection Agencies (USEPA) standard (2012) and National Agency For Food, Drug Administration And Control (NAFDAC) in Nigeria to determine their suitability for drinking and domestic purposes. The variables determined are: pH ranges from 7.67 - 8.56 mg/l which is suggestive of neutral to alkaline in character, calcium ranges from 5.12 - 2416 mg/l, turbidity ranges from 1.16 - 15.32 mg/l, total dissolved solid (Tds) ranges from 90 - 366 mg/l and total hardness ranges from 58.65 - 187.37 mg/l, fall within WHO standard, are suggestive of concentration of detergent from soap, calcium, magnesium, suspended solid particles and colloidal matters from some of the water samples. While iron ranges from 0.08 - 0.16 mg/l, potassium ranges from 0.02 - 0.18 mg/l, chloride ranges from 30.03 - 120.13 mg/l, sulphate ranges from 1.03 - 5.36 mg/l, nitrate ranges from 0.01 - 0.23 mg/l, lead ranges from 0 - 0.01 mg/l, Zinc ranges from 0 - 0.08 mg/l, copper ranges from 0 - 0.02 mg/l and magnesium ranges from 1.38 - 6.56 mg/l, fall within standards. Coliform count ranges from 0 - 14 mg/l. The water should be treated before the consumption because of its high concentration of detergent, suspended particles, faecal materials and calcium from the water samples. The quarry pits should be reclaimed and rehabilitate after mining. Alkaline materials should be used to neutralize the rock pile area, dumped site, tailing and mine pit itself to avoid acid generation.

An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

An adaptive neuro-fuzzy inference system white-box model for real-time multiphase flowing bottom-hole pressure prediction in wellbores

The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are used.This is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental datasets.In addition,most machine learning(ML)FBHP prediction models were constructed with real-time well data points and published without any visible mathematical equation.This makes it difficult for other readers to use these ML models since the datasets used in their development are not open-source.This study presents a white-box adaptive neuro-fuzzy inference system(ANFIS)model for real-time prediction of multiphase FBHP in wellbores.1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi eSugeno fuzzy inference systems(FIS)structures.The dataset was divided into two sets;80%for training and 20%for testing.Statistical performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction performance.The optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter sets.Trend analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on FBHP.In addition,graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models,empirical correlations,and machine learning models.New training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.