30 research outputs found

    Bilateral Heterogeneity in an Upwelling Mantle via Double Subduction of Oceanic Lithosphere

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    Vietnam is a major field of Cenozoic volcanism in Southeast (SE) Asia. Two contrasting models have been proposed to explain the mantle upwelling and volcanism in this region; collision of the Indian and Eurasian continents or subduction of the Pacific or Indo-Australian oceanic lithosphere. To place constraints on the origin of the intraplate volcanism in SE Asia, new geochronological and geochemical data for Cenozoic basalts in Vietnam are presented. Based largely on Sr-Nd-Pb isotope systematics, it was found that the sources of basalts from Central and Southern Vietnam are chemically distinct forming a sharp boundary at 13.5°N. The basalts north of the boundary show isotopic features similar to Enriched Mantle type 2 (EM2) ocean island basalts. Whereas the basalts south of the boundary show isotopic features similar to Enriched Mantle type 1 (EM1) ocean island basalts. The EM1 and EM2 basalts display positive Sr anomalies and elevated Pb/Ce and Th/La ratios, respectively. Such features suggest the origins of the sources through the recycling of deeply-subducted crustal lithologies. Furthermore, subduction of dense oceanic lithosphere can induce a convecting cell in the upper mantle. Therefore, we suggest that the chemically different basalts from Central and Southern Vietnam represent the surface expression of melting in two different convecting cells, one is driven by subduction of the Pacific plate and the other by subduction of the Indo-Australian plate

    Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

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    Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to reduce the use of natural materials such as stone and sand. However, traditional methodology used to predict compressive strength and to find out an optimum mix for GPC is yet to be formulated, especially in cases of GPC using by-products as aggregates. In this study, we propose novel hybrid artificial intelligence (AI) approaches, namely a particle swarm optimization (PSO)-based adaptive network-based fuzzy inference system (PSOANFIS) and a genetic algorithm (GA)-based adaptive network-based fuzzy inference system (GAANFIS) to predict the 28-day compressive strength of GPC containing 100% waste slag aggregates. To construct and validate these models, 21 different mixes with 210 specimens were casted and tested. Three input parameters were used to predict the tested compressive strength of GPC, i.e., the sodium solution (NaOH) concentration (varied from 10 to 14 M), the mass ratio of alkaline activation solution to fly ash (AAS/FA), ranging from 0.4 to 0.5, and the mass ratio of sodium silicate (Na2SiO3) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC

    Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete

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    Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model’s performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R2 = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R2 = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance

    Badanie zapewnienia rozwoju przepływu w przybrzeżnych marginalnych pól naftowych w Wietnamie: Studium przypadku pola naftowego Ca Ngu Vang

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    Over the last few years, PetroVietnam has discovered and exploited several marginal oil fields such as Ca Ngu Vang, Te Giac Trang, Hai Su Den, Hai Su Trang, etc. however the reserves are modest. Test results received during drilling exploratory wells within these fields indicated that the maximum total daily production rate from the wells could promisingly range to about 20,000 barrels of oil per day (BOPD). Unfortunately, the optimum development of these offshore oil fields still offers numerous challenges to oil engineers due to the limitations of equipment and technology. Oil production activities worldwide show that if the daily production of an offshore oilfield is less than 20,000 BOPD, a connection of the marginal fields to their nearest larger oil field should be taken into consideration in order to efficaciously recover more crude oil. Often, this method of production requires a long subsea pipeline system. Besides, the transportation of the fluids from these fields to the processing platform will undergo several serious problems caused by the deposition of wax. All these matters should be handled to guarantee the performance of transportation. A number of models using PIPESIM, PIPEPHRASE and OLGA have been applied to design and examine the operations of the subsea pipeline in different working conditions. Results of the simulations proposed the use of passive insulation to economically eliminate wax deposition and recommended proper pipeline shutdown operations to minimize several problems related to flow assurance issues in the region of interest.W ciągu ostatnich kilku lat w Vietnamie odkryto i eksploatowano kilka marginalnych pól naftowych, takich jak Ca Ngu Vang, Te Giac Trang, Hai Su Den, Hai Su Trang, itd.… Jednak zasoby są skromne. Wyniki testów otrzymane podczas wiercenia odwiertów poszukiwawczych na tych polach wykazały, że maksymalny całkowity dzienny poziom wydobycia z odwiertów może potencjalnie sięgać około 20 000 baryłek ropy dziennie (BOPD). Niestety, optymalny rozwój tych przybrzeżnych pól naftowych nadal stwarza liczne wyzwania dla inżynierów naftowych ze względu na ograniczenia sprzętu i technologii. Działalność wydobywcza ropy naftowej na całym świecie pokazuje, że jeśli dzienna produkcja morskiego pola naftowego jest mniejsza niż 20 000 BOPD, należy wziąć pod uwagę połączenie pól marginalnych z ich najbliższym większym polem naftowym, aby efektywnie odzyskać więcej ropy. Często ta metoda produkcji wymaga długiego systemu rurociągów podmorskich. Poza tym transport płynów z tych pól na platformę obróbkową będzie wiązał się z kilkoma poważnymi problemami spowodowanymi osadzaniem się wosku. Wszystkie te sprawy powinny być załatwione, aby zagwarantować wykonanie transportu. Szereg modeli wykorzystujących PIPESIM, PIPEPHRASE i OLGA zostało zastosowanych do projektowania i badania działania rurociągu podmorskiego w różnych warunkach pracy. W wynikach symulacji zaproponowano zastosowanie izolacji pasywnej w celu ekonomicznego wyeliminowania osadzania się wosku oraz zalecono prawidłowe operacje wyłączania rurociągu, aby zminimalizować kilka problemów związanych z kwestiami zapewnienia przepływu w obszarze zainteresowania
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