Globally, the contamination of water with arsenic is a serious health issue. Recently, several researches have endorsed the efficiency of biomass to remove As (III) via adsorption process, which is distinguished by its low cost and easy technique in comparison with conventional solutions. In the present work, biomass was prepared from indigenous Bacillus thuringiensis strain WS3 and was evaluated to remove As (III) from aqueous solution under different contact time, temperature, pH, As (III) concentrations and adsorbent dosages, both experimentally and theoretically. Subsequently, optimal conditions for As (III) removal were found; 6 (ppm) As (III) concentration at 37 °C, pH 7, six hours of contact time and 0.50 mg/ml of biomass dosage. The maximal As (III) loading capacity was determined as 10.94 mg/g. The equilibrium adsorption was simulated via the Langmuir isotherm model, which provided a better fitting than the Freundlich model. In addition, FESEM-EDX showed a significant change in the morphological characteristic of the biomass following As (III) adsorption. 128 batch experimental data were taken into account to create an artificial neural network (ANN) model that mimicked the human brain function. 5-7-1 neurons were in the input, hidden and output layers respectively. The batch data was reserved for training (75%), testing (10%) and validation process (15%). The relationship between the predicted output vector and experimental data offered a high degree of correlation (R2 = 0.9959) and mean squared error (MSE; 0.3462). The predicted output of the proposed model showed a good agreement with the batch work with reasonable accuracy.
Alkali activated concretes have emerged as a prospective alternative to conventional concrete wherein diverse waste materials have been converted as valuable spin-offs. This paper presents a wide experimental study on the sustainability of employing waste sawdust as a fine/coarse aggregate replacement incorporating fly ash (FA) and granulated blast furnace slag (GBFS) to make high-performance cement-free lightweight concretes. Waste sawdust was replaced with aggregate at 0, 25, 50, 75, and 100 vol% incorporating alkali binder, including 70% FA and 30% GBFS. The blend was activated using a low sodium hydroxide concentration (2 M). The acoustic, thermal, and predicted engineering properties of concretes were evaluated, and the life cycle of various mixtures were calculated to investigate the sustainability of concrete. Besides this, by using the available experimental test database, an optimized Artificial Neural Network (ANN) was developed to estimate the mechanical properties of the designed alkali-activated mortar mixes depending on each sawdust volume percentage. Based on the findings, it was found that the sound absorption and reduction in thermal conductivity were enhanced with increasing sawdust contents. The compressive strengths of the specimens were found to be influenced by the sawdust content and the strength dropped from 65 to 48 MPa with the corresponding increase in the sawdust levels from 0% up to 100%. The results also showed that the emissions of carbon dioxide, energy utilization, and outlay tended to drop with an increase in the amount of sawdust and show more the lightweight concrete to be more sustainable for construction applications.
Although free-cement-based alkali-activated paste, mortar, and concrete have been recognised as sustainable and environmental-friendly materials, a considerable amount of effort is still being channeled to ascertain the best binary or ternary binders that would satisfy the requirements of strength and durability as well as environmental aspects. In this study, the mechanical properties of alkali-activated mortar (AAM) made with binary binders, involving fly ash (FA) and granulated blast-furnace slag (GBFS) as well as bottle glass waste nano-silica powder (BGWNP), were opti-mised using both experimentally and optimisation modelling through three scenarios. In the first scenario, the addition of BGWNP varied from 5% to 20%, while FA and GBFS were kept constant (30:70). In the second and third scenarios, BGWNP (5-20%) was added as the partial replacement of FA and GBFS, separately. The results show that the combination of binary binders (FA and GBFS) and BGWNP increased AAM's strength compared to that of the control mixture for all scenarios. In addition, the findings also demonstrated that the replacement of FA by BGWNP was the most significant, while the effect of GBFS replacement by BGWNP was less significant. In particular, the highest improvement in compressive strength was recorded when FA, GBFS, and BGWNP were 61.6%, 30%, and 8.4%, respectively. Furthermore, the results of ANOVA (p values < 0.0001 and high F-values) as well as several statistical validation methods (R > 0.9, RAE < 0.1, RSE < 0.013, and RRSE < 0.116) confirmed that all the models were robust, reliable, and significant. Similarly, the data variation was found to be less than 5%, and the difference between the predicted R2 and adj. R2 was very small (<0.2), thus confirming that the proposed non-linear quadratic equations had the capability to predict for further observation. In conclusion, the use of BGWNP in AAM could act as a beneficial and sustainable strategy, not only to address environmental issues (e.g., landfill) but to also enhance strength properties.
The present study aimed to investigate the removal efficiency of cephalexin (CFX) by a novel Cu-Zn bionanocomposite biosynthesized in the secondary metabolic products of Aspergillus arenarioides EAN603 with pumpkin peels medium (CZ-BNC-APP). The optimization study was performed based on CFX concentrations (1, 10.5 and 20 ppm); CZ-BNC-APP dosage (10, 55 and 100 mg/L); time (10, 55 and 100 min), temperature (20, 32.5 and 45 °C). The artificial neural network (ANN) model was used to understand the CFX behavior for the factors affecting removal process. The CZ-BNC-APP showed an irregular shape with porous structure and size between 20 and 80 nm. The FTIR detected CC, C-O and OH groups. ANN model revealed that CZ-BNC-APP dosage exhibited the vital role in the removal process, while the removal process having a thermodynamic nature. The CFX removal was optimized with 12.41 ppm CFX, 60.60 mg/L of CZ-BNC-APP, after 97.55 min and at 35 °C, the real maximum removal was 95.53% with 100.52 mg g-1 of the maximum adsorption capacity and 99.5% of the coefficient. The adsorption of CFX on CZ-BNC-APP was fitted with pseudo-second-order model and both Langmuir and Freundlich isotherms models. These findings revealed that CZ-BNC-APP exhibited high potential to remove CFX.
Exposing concrete to high temperatures leads to harmful effects in its mechanical and microstructural properties, and ultimately to total failure. In this sense, various types of waste materials are exploited not only to tackle serious environmental issues but also to enhance the thermal stability of concrete exposed to elevated temperatures. Furthermore, nanomaterials have been incorporated in concrete as admixtures to reduce the thermal degradation of concrete due to exposure to high temperatures. In the present study, the effects of nanosilica (NS) incorporation on the properties of concrete subjected to elevated temperature are discussed in several sequential sections. The process mechanism of concrete deterioration due to fire exposure and the important factors that could affect the performance of concrete under fire were evaluated. Moreover, brief highlights on the effect of elevated temperature on concrete containing waste materials are included in this review paper. Reviews and summaries of the available and updated literature regarding concrete containing NS are considered. According to the findings of the studies under review, the addition of nanosilica to concrete contributed in reduced strength loss, minimized internal porosity, and enhanced matrix compactness in concrete.
Excessive accumulation of waste materials has presented a serious environmental problem on a global scale. This has prompted many researchers to utilise agricultural, industrial, and by-product waste materials as the replacement of aggregate in the concrete matrix. In this present study, the prediction and optimisation of coconut shell (CA) content as the replacement of fine aggregate were evaluated based on the mechanical properties of the concrete (M30). Based on the suggested design array from the response surface methodology (RSM) model, experimental tests were carried out to achieve the goal of this study. The collected data was used to develop mathematical predictive equations using both GEP and RSM models. Analysis of variance (ANOVA) was also taken into account to appraise and verify the performance of the proposed models. Based on the results, the optimum content of replacing CA was 50%. In particular, the compressive, tensile, and flexural strength obtained after 28 days of curing were 46.2, 3.74, and 8.06 MPa, respectively, from the RSM model and 46.18, 3.85, and 7.99 MPa, respectively, from the GEP model. The obtained values were superior to those of the control concrete sample (43.12, 3.51 and 7.14 MPa, respectively). Beyond the optimum content, a loss in strength was observed. It was also found that both the GEP and RSM models exhibited high prediction accuracy with strong correlations (R2 = 0.97 and 0.95, respectively). In addition, minimum prediction error (RMSE
The development of self-compacting alkali-activated concrete (SCAAC) has become a hot topic in the scientific community; however, most of the existing literature focuses on the utilization of fly ash (FA), ground blast furnace slag (GBFS), silica fume (SF), and rice husk ash (RHA) as the binder. In this study, both the experimental and theoretical assessments using response surface methodology (RSM) were taken into account to optimize and predict the optimal content of ceramic waste powder (CWP) in GBFS-based self-compacting alkali-activated concrete, thus promoting the utilization of ceramic waste in construction engineering. Based on the suggested design array from the RSM model, experimental tests were first carried out to determine the optimum CWP content to achieve reasonable compressive, tensile, and flexural strengths in the SCAAC when exposed to ambient conditions, as well as to minimize its strength loss, weight loss, and UPVL upon exposure to acid attack. Based on the results, the optimum content of CWP that satisfied both the strength and durability aspects was 31%. In particular, a reasonable reduction in the compressive strength of 16% was recorded compared to that of the control specimen (without ceramic). Meanwhile, the compressive strength loss of SCAAC when exposed to acid attack minimized to 59.17%, which was lower than that of the control specimen (74.2%). Furthermore, the developed RSM models were found to be reliable and accurate, with minimum errors (RMSE < 1.337). In addition, a strong correlation (R > 0.99, R2 < 0.99, adj. R2 < 0.98) was observed between the predicted and actual data. Moreover, the significance of the models was also proven via ANOVA, in which p-values of less than 0.001 and high F-values were recorded for all equations.
The concern about coconut shell disposal and natural fine aggregate depletion has prompted researchers to utilize coconut shell as aggregate in recent years. However, the majority of the present literature has focused on utilizing coconut shell as a coarse aggregate replacement in concrete via the traditional method. In this study, concrete incorporating coconut shell as a fine aggregate replacement (10-100%) was evaluated using permeability and water absorption tests in a systematic way. The response surface methodology (RSM) was first used to design the experimental works. In addition, an artificial neural network (ANN) and genetic expression programming (GEP) were also taken into account to mathematically predict the permeability and water absorption. Based on both experimental and theoretical modeling, three scenarios were observed. In the first scenario, high quality concrete was achieved when the replacement percentage of sand by coconut shell ranged from 0% to 10%. This is because both the permeability and water absorption were less than 1.5 × 10-11 m and 5%, respectively. In the second scenario, an acceptable and reasonable low permeability (less than 2.7 × 10-11 m/s) and water absorption (less than 6.7%) were also obtained when the replacement percentage increased up to 60%. In contrast, the high content coconut shell, such as 90% and 100%, developed concrete with a high permeability and water absorption and was defined in the third scenario. It was also inferred that both the experimental and mathematical models (ANN, GEP, and RSM) have consistent and accurate results. The correlation statistics indicators (R2) were greater than 0.94 and the error was less than 0.3, indicating a strong correlation and minimum error. In conclusion, coconut shell could act as a good alternative material to produce cleaner concrete with an optimum value of 50% as a fine aggregate replacement.