Identifying the Barriers to Sustainable Management of Construction and Demolition Waste in Developed and Developing Countries

The construction industry is a vital part of every nation’s economy. Construction activities influence the social, environmental, and economic aspects of sustainability. There are so many barriers to sustainable construction and demolition waste management (C&DWM). This study aims to identify barriers for effective sustainable C&DWM in developed and developing countries. To achieve the objective, 11 barriers have been selected and identified based on an excessive and comprehensive literature review, and then reviewed by experts. These reviewed barriers were further examined by various experts within different organizations using a questionnaire survey. Ranking of the barriers was carried out using the Relative Importance Index (RI), and the results were statistically analyzed using Statistical Package for Social Sciences (SPSS). Practical solutions were proposed to overcome the identified barriers. The overall ranking of barriers by RI indicates that insufficient attention paid to C&DWM, lack of law enforcement, lack of regulation, and financial constraints represent the four major barriers to sustainable C&DWM in these countries. The findings of this study and the proposed solutions are enablers for decision-makers to develop effective strategies to tackle construction and demolition wastes in sustainable manners

Reduction of ash sintering precursor components in rice straw by water washing

The thermal conversion of rice straw is an attractive option for recovering its energy, but the process requires exhaustive control because of ash-related problems. Straw washing is one method of reducing ash-related problems and improving combustion behaviour. In this study, the ash of washed and unwashed rice straw samples was chemically characterized, tested using thermogravimetric analysis and environmental scanning electron microscopy (ESEM), and subjected to higher combustion temperatures in a muffle furnace. Results showed that silicon was the most important component in the ash. Furthermore, a reduction in undesirable inorganic compounds related to ash problems, such as chlorine and potassium, was achieved by washing the straw samples. This practice could improve thermal behaviour and decrease the sintering formation of ash.This research was funded by the Innovation and Science Division of the Andalusian Regional Government (Research Projects P08-RNM-03584 and TIC-02913), the Spanish Ministry of Science and Innovation (Research Project CTM2009-07199), and the Spanish Agency for International Development Cooperation (AECID) of the Ministry of Foreign Affairs and Cooperation (Project AP/045946/11)

Adsorption mechanism and modelling of hydrocarbon contaminants onto rice straw activated carbons

The adsorption of Diphenolic acid (DPA), 2,4-Dichlorophenoxyacetic acid (2,4-D), and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were examined in aqueous solution using activated carbon rice straw. The rice straw was activated by using two reagents, zinc chloride and phosphoric acid and named as RSZ, RSP, respectively. The results showed that both carbons have a relatively high adsorption capacity. Concerning the adsorption kinetic, the second-order model has better fit than the first model to experimental data. The adsorption yield of both carbons increased in the order: DPA < 2,4-D < MCPA. The pore volume diffusion model satisfactorily fitted the experiment on both carbons. Furthermore, solution pH has a high influence on the adsorption capacity for both carbons. The adsorption mechanism of selected pollutants onto carbon samples has been controlled by dispersion interaction π-π electrons and electrostatic interaction, moreover, the contribution of pore volume diffusion is the controlling mechanism of the overall rate of adsorption

Adsorption mechanism and modelling of hydrocarbon contaminants onto rice straw activated carbons

The adsorption of Diphenolic acid (DPA), 2,4-Dichlorophenoxyacetic acid (2,4-D), and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were examined in aqueous solution using activated carbon rice straw. The rice straw was activated by using two reagents, zinc chloride and phosphoric acid and named as RSZ, RSP, respectively. The results showed that both carbons have a relatively high adsorption capacity. Concerning the adsorption kinetic, the second-order model has better fit than the first model to experimental data. The adsorption yield of both carbons increased in the order: DPA < 2,4-D < MCPA. The pore volume diffusion model satisfactorily fitted the experiment on both carbons. Furthermore, solution pH has a high influence on the adsorption capacity for both carbons. The adsorption mechanism of selected pollutants onto carbon samples has been controlled by dispersion interaction π-π electrons and electrostatic interaction, moreover, the contribution of pore volume diffusion is the controlling mechanism of the overall rate of adsorption

Adsorption of Diphenolic Acid from Contaminated Water onto Commercial and Prepared Activated Carbons from Wheat Straw

The fabrication of carbon materials from biomass residues can be a promising economical approach for absorbing various target pollutants from aqueous phase. In the study, the adsorption of diphenolic acid (DPA) is investigated on activated carbons fabricated from wheat straw (ACWS) and commercial-activated carbon cloth (CACC). Adsorption kinetics, isotherms, and operational variables (solution pH and ionic strength) are analyzed for the adsorption capacity of the DPA on both carbons. The results show that the ACWS has a higher surface area (1164 m2/g) and volume of micropores (0.51 cm3/g) than those of the CACC. The second-order kinetics model fitted the experiment data better than the first kinetics models with a lower percentage of deviation. The adsorption capacity of the ACWS (264.90 mg/g) is higher than the CACC (168.19 mg/g) because of the higher surface area and volume of micropores of the ACWS. The adsorption isotherm shows that the adsorption of the DPA on the ACWS and CACC is consistent with the Langmuir and Freundlich isotherm models, respectively. The pH has a significant effect on DPA adsorption onto both carbons. The adsorption process is favored at the acidic pH, but the presence of electrolytes has no effect on the adsorption capacity of both carbons due to the screening effect. Thus, the preparation of activated carbon from wheat straw is an attractive option to recycle the wheat straw to added-value materials that can be used for the removal of such pollutants from aqueous solution. These findings can increase the research knowledge about the management of different straws in a sustainable way to produce activated carbon for different applications

Optimization and Modelling the Mechanical Performance of Date Palm Fiber-Reinforced Concrete Incorporating Powdered Activation Carbon Using Response Surface Methodology

Date palm fiber (DPF) has been reported to have many advantages when used in concrete, however, its major disadvantage is that it causes a reduction in compressive strength. In this research, powdered activated carbon (PAC) was added to cement in the DPF-reinforced concrete (DPFRC) to lessen the loss in strength. PAC has not been properly utilized as an additive in fiber reinforced concrete even though it has been reported to enhance the properties of cementitious composites. Response surface methodology (RSM) has also been utilized for experimental design, model development, results analysis, and optimization. The variables were DPF and PAC as additions each at proportions of 0%, 1%, 2%, and 3% by weight of cement. Slump, fresh density, mechanical strengths, and water absorption were the responses that were considered. From the results, both DPF and PAC decreased the workability of the concrete. DPF addition improved the splitting tensile and flexural strengths and reduced the compressive strength, and up to 2 wt% PAC addition enhanced the concrete’s strength and lowered the water absorption. The proposed models using RSM were extremely significant and have excellent predictive power for the concrete’s aforementioned properties. Each of the models was further validated experimentally and was found to have an average error of less than 5.5%. According to the results of the optimization, the optimal mix of 0.93 wt% DPF and 0.37 wt% PAC as cement additives resulted in the best properties of the DPFRC in terms of workability, strength, and water absorption. The optimization’s outcome received a 91% desirability rating. The addition of 1% PAC increased the 28-day compressive strength of the DPFRC containing 0%, 1% and 2% DPF by 9.67%, 11.13% and 5.5% respectively. Similarly, 1% PAC addition enhanced the 28-day split tensile strength of the DPFRC containing 0%, 1% and 2% by 8.54%, 11.08% and 19.3% respectively. Likewise, the 28-day flexural strength of DPFRC containing 0%, 1%, 2% and 3% improved by 8.3%, 11.15%, 18.7% and 6.73% respectively with the addition of 1% PAC. Lastly, 1% PAC addition led to a reduction in the water absorption of DPFRC containing 0% and 1% DPF by 17.93% and 12.2% respectively

Adsorption mechanism and modelling of hydrocarbon contaminants onto rice straw activated carbons

The adsorption of Diphenolic acid (DPA), 2,4-Dichlorophenoxyacetic acid (2,4-D), and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were examined in aqueous solution using activated carbon rice straw. The rice straw was activated by using two reagents, zinc chloride and phosphoric acid and named as RSZ, RSP, respectively. The results showed that both carbons have a relatively high adsorption capacity. Concerning the adsorption kinetic, the second-order model has better fit than the first model to experimental data. The adsorption yield of both carbons increased in the order: DPA < 2,4-D < MCPA. The pore volume diffusion model satisfactorily fitted the experiment on both carbons. Furthermore, solution pH has a high influence on the adsorption capacity for both carbons. The adsorption mechanism of selected pollutants onto carbon samples has been controlled by dispersion interaction π-π electrons and electrostatic interaction, moreover, the contribution of pore volume diffusion is the controlling mechanism of the overall rate of adsorption

Effect of Alkaline Pretreatment on the Characteristics of Barley Straw and Modeling of Methane Production via Codigestion of Pretreated Straw with Sewage Sludge

Straw pretreatment enhances the cellulose accessibility and increases the methane yield from anaerobic digestion. This study investigated the effects of alkali pretreatments with different chemical agents (NaOH, KOH, and Na2CO3) on the physicochemical and thermal characteristics of barley straw, as well as methane production from codigestion with sewage sludge. Artificial neural network modeling with a feedforward neural network (FFNN) and slime mold optimization (SMO) techniques were used to predict methane production. NaOH pretreatment was shown to be the best pretreatment for removing hemicellulose and lignin and for increasing the cellulose accessibility. Moreover, there was a 2.57-fold higher level of methane production compared to that from codigestion with untreated straw. The removal ratios for the total solids, volatile solids, and chemical oxygen demand reached 59.3, 67.2, and 73.4%, respectively. The modeling results showed that the FFNN-SMO method can be an effective tool for simulating the methane generation process, since training, validating, and testing produced very high correlation coefficients. The FFNN-SMO accurately predicted the amount of methane produced, with an R2 of 0.998 and a 3.1x10-5 root mean square error (RMSE)

Groundwater Quality Assessment for Drinking and Irrigation Purposes at Al-Jouf Area in KSA Using Artificial Neural Network, GIS, and Multivariate Statistical Techniques

Groundwater is an essential resource for drinking and agricultural purposes in the Al-Jouf region, Saudi Arabia. The main objective of this study is to assess groundwater quality for drinking and irrigation purposes in the Al-Jouf region. Physicochemical characteristics of groundwater were determined, including total dissolved solids (TDS), pH, electric conductivity (EC), hardness, and various anions and cations. The groundwater quality index (WQI) was calculated to determine the suitability of groundwater for drinking purposes. The EC, sodium percentage (Na+ %), magnesium hazard (MH), sodium adsorption ratio (SAR), potential salinity (PS), and Kelley’s ratio (KR) were assessed to evaluate the suitability of groundwater for irrigation. Effective statistical tests and Feed-forward neural network (FFNN) modeling were applied to reveal the correlation between parameters and predict WQI. The results indicated that approximately all samples are appropriate for drinking and irrigation uses except samples of the Al Qaryat region. The ionic abundance ranking was Na+ > Ca2+ > Mg2+ > K+ for cations, and Cl− > SO42− > NO3− for anions. Moreover, the groundwater is dominated by alkali metals (K+ and Na+) and controlled by the rock–water interaction process. The indicators of groundwater quality for irrigation and drinking according to the following criteria (Na+ %, SAR, KR, MH, PS, WQI (WHO), and WQI (BIS)) can be predicted by the FFNN with root mean square errors (RMSE) of 0.136, 0.070, 0.022, 0.073, 2.45 × 10−3, 1.45 × 10−2, and 1.18 × 10−2, respectively, and R2 of 0.99, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00, respectively

Effect of Alkaline Pretreatment on the Characteristics of Barley Straw and Modeling of Methane Production via Codigestion of Pretreated Straw with Sewage Sludge

Straw pretreatment enhances the cellulose accessibility and increases the methane yield from anaerobic digestion. This study investigated the effects of alkali pretreatments with different chemical agents (NaOH, KOH, and Na2CO3) on the physicochemical and thermal characteristics of barley straw, as well as methane production from codigestion with sewage sludge. Artificial neural network modeling with a feedforward neural network (FFNN) and slime mold optimization (SMO) techniques were used to predict methane production. NaOH pretreatment was shown to be the best pretreatment for removing hemicellulose and lignin and for increasing the cellulose accessibility. Moreover, there was a 2.57-fold higher level of methane production compared to that from codigestion with untreated straw. The removal ratios for the total solids, volatile solids, and chemical oxygen demand reached 59.3, 67.2, and 73.4%, respectively. The modeling results showed that the FFNN-SMO method can be an effective tool for simulating the methane generation process, since training, validating, and testing produced very high correlation coefficients. The FFNN-SMO accurately predicted the amount of methane produced, with an R2 of 0.998 and a 3.1x10-5 root mean square error (RMSE)