Effect of banana tree leaves ash as cementitious material on the durability of concrete against sulphate and acid attacks.

The construction industry's rapid growth poses challenges tied to raw material depletion and increased greenhouse gas emissions. To address this, alternative materials like agricultural residues are gaining prominence due to their potential to reduce carbon emissions and waste generation. In this context this research optimizes the use of banana leaves ash as a partial cement substitution, focusing on durability, and identifying the ideal cement-to-ash ratio for sustainable concrete. For this purpose, concrete mixes were prepared with BLA replacing cement partially in different proportions i.e. (0 %, 5 %, 10 %, 15 %, & 20 %) and were analyzed for their physical, mechanical and Durability (Acid and Sulphate resistance) properties. Compressive strength, acid resistance and sulphate resistance testing continued for 90 days with the intervals of 7, 28 and 90 days. The results revealed that up to 10 % incorporation of BLA improved compressive strength by 10 %, while higher BLA proportions (up to 20 %) displayed superior performance in durability tests as compared to the conventional mix. The results reveal the potentials of banana leave ash to refine the concrete matrix by formation of addition C-S-H gel which leads towards a better performance specially in terms of durability aspect. Hence, banana leaf ash (BLA) is an efficient concrete ingredient, particularly up to 10 % of the mix. Beyond this threshold, it's still suitable for applications where extreme strength isn't the primary concern, because there may be a slight reduction in compressive strength.

Modeling of success factors of using PU coats in concrete construction projects.

Polyurea coatings are well recognized for their remarkable protective properties, making them highly appropriate for practical use in the field of concrete building. The use of polyurea coatings in the concrete building business is currently constrained, despite its prevalent application in industrialized nations. The limited use may be ascribed to ambiguities about the determinants of effective implementation in this particular setting, as well as the dearth of extensive study in the realm of new building materials. The primary objective of this research is to assess and conceptualize the key determinants linked to the use of polyurea coatings in concrete building endeavors. Utilizing a quantitative research approach, a comprehensive literature analysis was conducted to identify a total of 21 probable success variables. The reliability of the questionnaire was established by the administration of a pilot survey, and afterwards, an exploratory factor analysis (EFA) was performed to enhance the clarity and precision of the underlying components. The researchers used structural modeling (SEM) approaches to develop a robust model using the primary data obtained from the questionnaire survey. The EFA revealed the presence of five unique constructs that have an impact on the effectiveness of polyurea coatings in concrete building projects. These constructions comprise several characteristics, including environmental considerations, functional requirements, protective properties, execution processes, and creative elements. The significance and relevance of this research are shown by the validation of the study's results using SEM. The study makes a valuable contribution towards the progression of polyurea coating use within the concrete building sector.

Electrochemical and DFT Studies of the Pistacia Integerrima Gall Extract: An Eco-friendly Approach towards the Corrosion of Steel in Acidic Medium.

A novel application of the Pistacia integerrima gall extract as an environmentally friendly corrosion inhibitor is reported in this study. The major phytochemicals present in the gall extract, namely pistagremic acid, ß-sitosterol, pistiphloroglucinyl ether, pistaciaphenyl ester, naringenin, and 5,7-dihydroxy-2-(4-hydroxyphenyl)-2,3-dihydrochromen-4-one, play key roles in its anticorrosive behavior on steel in aggressive media. Several approaches were used to study the corrosion prevention activity of steel in 1 M H2SO4, including weight loss analysis, scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), and density functional theory (DFT). At 2000 mg L-1, the highest efficiency of 92.19% was observed in 1 M H2SO4. An SEM study was conducted to validate the surface coverage of the metal surface. DFT studies revealed several nucleophilic regions present in the phytochemicals of the inhibitor, which supported the favorable nucleophilicity. Corrosion studies have not been performed on this sample. Phytochemicals make it an effective corrosion inhibitor, and its extraction process utilizes distilled water, making it better than other inhibitors. It has been proven that the obtained values of ΔEInhDFT for pistiphloroglucinyl, pistaciaphenyl ether, and naringenin organic compounds were very low, confirming the high reactivity of these corrosion inhibitors. The order of the values of ΔEInhDFT is as follows: pistaciaphenyl ether > pistiphloroglucinyl ether > naringenin organic compound; this suggests that pistaciaphenyl ether is more reactive than the other compounds. In this study, P. integerrima gall extract emerges as a novel and highly effective corrosion resistance agent in 1 M H2SO4, chosen for its relevance to acid pickling and cleaning processes.

Effect of wheat straw ash as cementitious material on the mechanical characteristics and embodied carbon of concrete reinforced with coir fiber.

The use of supplementary cementitious materials has been widely accepted due to increasing global carbon emissions resulting from demand and the consequent production of Portland cement. Moreover, researchers are also working on complementing the strength deficiencies of concrete; fiber reinforcement is one of those techniques. This study aims to assess the influence of recycling wheat straw ash (WSA) as cement replacement material and coir/coconut fibers (CF) as reinforcement ingredients together on the mechanical properties, permeability and embodied carbon of concrete. A total of 255 concrete samples were prepared with 1:1.5:3 mix proportions at 0.52 water-cement ratio and these all-concrete specimens were cured for 28 days. It was revealed that the addition of 10 % WSA and 2 % CF in concrete were recorded the compressive, splitting tensile and flexural strengths by 33 MPa, 3.55 MPa and 5.16 MPa which is greater than control mix concrete at 28 days respectively. Moreover, it was also observed that the permeability of concrete incorporating 4 % of coir fiber and 20 % of WSA was reduced by 63.40 % than that of the control mix after 28 days which can prevent the propagation of major and minor cracks. In addition, the embodied carbon of concrete is getting reduced when the replacement level of cement with WSA along with CF increases in concrete. Furthermore, based on the results obtained, the optimum amount of WSA was suggested to be 10 % and that of coir fiber reinforcement was suggested to be 2 % for improved results.

Utilizing nanotechnology to boost the reliability and determine the vertical load capacity of pile assemblies.

Because of their high electrocatalytic activity, sensitivity, selectivity, and long-term stability in electrochemical sensors and biosensors, numerous nanomaterials are being used as suitable electrode materials thanks to developments in nanotechnology. Electrochemical sensors and biosensors are two areas where two-dimensional layered materials (2DLMs) are finding increasing utility due to their unusual structure and physicochemical features. Nanosensors, by their unprecedented sensitivity and minute scale, can probe deeper into the structural integrity of piles, capturing intricacies that traditional tools overlook. These advanced devices detect anomalies, voids, and minute defects in the pile structure with unparalleled granularity. Their effectiveness lies in detection and their capacity to provide real-time feedback on pile health, heralding a shift from reactive to proactive maintenance methodologies. Harvesting data from these nanosensors, data was incorporated into a probabilistic model, executing the reliability index calculations through Monte Carlo simulations. Preliminary outcomes show a commendable enhancement in the predictability of vertical bearing capacity, with the coefficient of variation dwindling by up to 12%. The introduction of nanosensors facilitates instantaneous monitoring and fortifies the long-term stability of pile foundations. This study accentuates the transformative potential of nanosensors in geotechnical engineering.

Seaweed biomass as a sustainable resource for synthesis of ZnO nanoparticles using Sargassum wightii ethanol extract and their environmental and biomedical applications through Gaussian mixture model.

Zinc oxide nanoparticles (ZnO) possess unique features that mak them a common matter among different industries. Nevertheless, traditional models of synthesizing ZnO-NPs are related with health and environmental and risks due to harmful chemicals. The biosynthesis of zinc oxide nanoparticles was achieved using the hot water extract of Sargassum wightii (SW), which serves as a reducing agent. This extract is mixed with zinc precursors, initiating a bio-reduction process. UV-vis, FTIR, XRD, Raman, DLS, SEM, EDX, TEM imaging, and XPS analysis are used. The novelty of this research lies in utilizing a bio-reduction process involving hot water extract of SW to synthesize zinc oxide nanoparticles, providing a safer and eco-friendly alternative to traditional chemical methods. Here, the zinc oxide nanoparticles produced through the biosynthesis process effectively addressed oral infections (Streptococcus mutans) due to their ability to disrupt the integrity of bacterial cell membranes, interfere with cellular processes, and inhibit the growth and proliferation of bacteria responsible for oral infections. Gaussian Mixture Models (GMMs) uncover intricate patterns within medical data, enabling enhanced diagnostics, treatment personalization, and patient outcomes. This study aims to apply Gaussian Mixture Models (GMMs) to medical data for subpopulation identification and disease subtyping, contributing to personalized treatment strategies and improved patient care. With a dataset comprising 300 samples, the application of GMM showed lower BIC and AIC values (2500, 3200), a high Silhouette Score (0.65 from -1 to 1) reflecting well-defined clusters, Calinski-Harabasz (120) and Davies-Bouldin Indices (0.45). These metrics collectively underscored the model's success in revealing distinct patterns within the data. ZnO-nanocoated aligners were effective against Streptococcus mutans, with the maximum antibacterial effect observed for 2 days and lasting for 7 days.

Validation of the efficiency of arsenic mitigation strategies in southwestern region of Bangladesh and development of a cost-effective adsorbent to mitigate arsenic levels.

World's highest arsenic (As) contamination is well-documented for the groundwater system of southwestern region (mainly Jashore district) of Bangladesh, where the majority of inhabitants are underprivileged. To mitigate As poisoning in southwestern Bangladesh, numerous steps have been taken so far by the government and non-governmental organizations (NGOs). Among them, digging deep tube wells and As removal by naturally deposited Fe(OH)3 species are being widely practiced in the contaminated areas. However, these actions have been left unmonitored for decades, making people unaware of this naturally occurring deadly poison in their drinking water. Hence, water samples (n = 63, both treated and untreated) and soil samples (n = 4) were collected from different spots in Jashore district to assess the safety level of drinking water and to understand the probable reasons for high As(III) contamination. About 93.7% of samples were found to contain As(III) above 10 µg/L; among them, 38% contained above 50 µg/L. The study shows that current As(III) removal strategies in the study area are ineffective. In this connection, a simple low-cost As(III) removal adsorbent is proposed that can be prepared with very cheap and locally available materials like iron sludge and charcoal. The adsorbent was characterized in terms of SEM, EDX, and XPS. The optimal dosage of the adsorbent was investigated for real-life application concerning several vital water quality parameters. The Fe-C adsorbent exhibited a maximum As(III) removal efficiency of 92% in real groundwater samples. The study will allow policymakers for informed decision-making regarding water body management as well as enable the local people to avail As-safe water in a way that aligns with their economic factors.

Techno-economic optimization and Nox emission reduction through steam injection in gas turbine combustion chamber for waste heat recovery and water production.

Considering the persistent human need for electricity and fresh water, cogeneration systems based on the production of these two products have attracted the attention of researchers. This study investigates a cogeneration system of electricity and fresh water based on gas turbine (GT) as the prime mover. The wasted energy of the GT exhaust gases is absorbed by a heat recovery steam generator (HRSG) and supplies the superheat steam required by the steam turbine (ST). In order to produce fresh water, a multi-effect desalination (MED) system is applied. The motive steam required is provided by extracting steam from the ST. In order to reduce the environmental pollution of this cogeneration system, the steam injection method is proposed in the GT's combustion chamber (CC). This system is optimized by a multi-objective optimization tool based on the Genetic Algorithm (GA). The design variables include pressure ratio of compressor (CPR), inlet temperature of gas turbine (TIT), steam injection mass flow rate in the CC, HRSG operating pressure, HRSG evaporator pinch point temperature difference (PPTD), steam pressure of the MED ejector, ejector motive steam flow rate, number of MED effects, and return effect. The goals are to minimize the total cost rate (TCR), which includes the cost of initial investment and maintenance of the system, the cost of consumed fuel, and the cost of disposing of CO and NO pollutants, as well as maximizing the exergy efficiency. In the end, it is observed that the steam injection in the CC leads to the reduction of the mentioned pollutant index, and it is proposed as a suitable solution to reduce the pollution of the proposed cogeneration system.

Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures.

Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants. This research proposes a novel technique in optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cell for saline water treatment and the saline composition has been analyzed using a gradient discriminant random field. Experimental analysis is carried out in terms of specificity, computational cost, kappa coefficient, training accuracy, and mean average precision for various tunnelling electron microscope (TEM) image datasets. The bright-field TEM (BF-TEM) dataset attained a specificity of 75%, kappa coefficient of 44%, training accuracy of 81%, and mean average precision of 61%, whereas the annular dark-field scanning TEM (ADF-STEM) dataset produced specificity of 79%, kappa coefficient of 49%, training accuracy of 85%, and mean average precision of 66% as compared with the existing artificial neural network (ANN) approach.

Performance assessment and multiobjective optimization of a biomass waste-fired gasification combined cycle for emission reduction.

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h.

Effect of calcined clay and marble dust powder as cementitious material on the mechanical properties and embodied carbon of high strength concrete by using RSM-based modelling.

In the last decade, there has been an increase in research on ecologically benign, cost-effective, and socially useful cement alternative materials for concrete. Alternatives involve industrial and agriculture waste, the potential advantages of which may be recognized by recycling, repurposing, and recreating techniques. Important energy reserves and a decrease in Portland cement (PC) consumption may be attained by using these wastes as supplementary and substitute ingredients, contributing to a reduction in carbon dioxide (CO2) production. Consequently, the incorporation of marble dust powder (MDP) and calcined clay (CC) as supplementary cementitious material (SCM) in high strength concrete may lower the environmental effect and reduce the amount of PC in mixes. This study is conducted on concrete containing 0%, 5%, 10%, 15%, and 20% of MDP and CC as cementitious materials alone and in combination. The main objectives of this investigations are to examine the effect of MDP and CC as cementitious materials on the flowability and mechanical characteristics of high strength concrete. In order to examine the ecological effect of CC and MDP, the eco-strength efficiency and embodied carbon were considered. In this context, there are so many trial mixes were performed on cubical specimens for achieving targeted compressive strength about 60 MPa after 28 days. After getting it, a total of 273 concrete specimens (cubes, cylinders, and prisms) were used to test the compressive, splitting tensile, and flexural strength of high strength concrete correspondingly. Moreover, when the amount of MDP and CC as SCM in the mixture grows, the workability of green concrete decreases. In addition, the compressive strength, flexural strength, and splitting tensile strength are increased by 6.38 MPa, 67.66 MPa, and 4.88 MPa, respectively, at 10% SCM (5% MDP and 5% CC) over a period of 28 days. In addition, using ANOVA, response prediction models were generated and confirmed at a 95% level of significance. The R2 values of the models varied from 96 to 99%. Furthermore, increasing the amount of CC and MDP as SCM in concrete also reduces the amount of carbon embedded in the material. It is recommended that the utilization of 10% SCM (5% MDP and 5% CC) in high strength concrete is providing optimum outcomes for construction industry in the field of Civil Engineering.

Deep convolutional neural network with sine cosine algorithm based wastewater treatment systems.

Wastewater treatment systems are essential in today's business to meet the ever-increasing requirements of environmental regulations while also limiting the environmental impact of the sector's discharges. A new control and management information system is needed to handle the residual fluids. This study advises that Wastewater Treatment System (WWTS) operators use intelligent technologies that analyze data and forecast the future behaviour of processes. This method incorporates industrial data into the wastewater treatment model. Deep Convolutional Neural Network (DCNN) and Since Cosine Algorithm (SCA), two powerful artificial neural networks, were used to predict these properties over time. Remediation actions can be taken to ensure procedures are carried out in accordance with the specifications. Water treatment facilities can benefit from this technology because of its sophisticated process that changes feature dynamically and inconsistently. The ultimate goal is to improve the precision with which wastewater treatment models create their predictions. Using DCNN and SCA techniques, the Chemical Oxygen Demand (COD) in wastewater treatment system input and effluent is estimated in this study. Finally, the DCNN-SCA model is applied for the optimization, and it assists in improving the predictive performance. The experimental validation of the DCNN-SCA model is tested and the outcomes are investigated under various prospects. The DCNN-SCA model has achieved a maximum accuracy performance and proving that it outperforms compare with the prevailing techniques over recent approaches. The DCNN-SCA-WWTS model has shown maximum performance Under 600 data, DCNN-SCA-WWTS has a precision of 97.63%, a recall of 96.37%, a F score of 95.31%, an accuracy of 96.27%, an RMSE of 27.55%, and a MAPE of 20.97%.