Annually, a substantial volume of food waste is being released into the environment. Restaurant food waste (RFW) valorization using microwave-assisted hydrolysis (MAH) is a sustainable approach to produce fermentable sugars. However, RFW is composed of different foodstuffs with different physicochemical, nutritional, and degradation rates. This study explored the typological, chemical, and elemental analysis of RFW. Results revealed that the four main types of RFW were vegetable (33.2 %), meat (19.3 %), rice (15.2 %), and bread waste (11.0 %). The key parameters impacting the MAH of typologically sorted RFW were identified using the Plackett–Burman design (PBD). Then the central composite design (CCD) with 30 runs was used to increase reducing sugar content (RSC). The optimized condition was as follows: temperature 96.0 °C, microwave power 340 W, HCl concentration of 1.45 %, and microwave heating time 11.1 min. The derived hydrolysates were characterized for their biochemical and monosaccharide composition.
Oil-contaminated water from oil and gas exploration remains the industry’s primary waste stream. The common method of using chemical coagulation/flocculation followed by air flotation has drawbacks such as generating non-biodegradable and toxic sludge and high operational costs. This study presents an eco-friendly alternative utilizing chitosan and beach sand to remove emulsified oil from water. Chitosan acts as a biodegradable flocculant, while beach sand aids in high-density floc formation and accelerates settling velocity. This approach achieved up to 94 % oil removal efficiency and reduced settling time from 90 to 15 min by using 100 mg/L chitosan and 500 mg/L beach sand with a particle size distribution of 50–100 μm. Shorter settling time reduces capital expenditure compared to conventional methods. Additionally, using natural materials like chitosan and beach sand minimizes toxic sludge generation. This eco-friendly approach offers a promising alternative to conventional methods for treating oily wastewater.
The growing need to obtain nanomaterials has resulted in a trend to avoid environmentally harmful methodologies involving chemicals that damage ecosystems and health by searching for natural reducers and stabilizers with zero polluting impact. In this research, zinc oxide nanoparticles were synthesized following an environmentally friendly synthesis methodology by using a natural extract of Bauhinia forficata that, thanks to its phytochemical composition rich in organic molecules such as polyphenols and flavonoids, allows the correct formation of nanoparticles by acting as stabilizers. The results of the characterizations show the proper formation of the nanoparticles and a direct relationship between the percentage used to obtain the nanoparticles and their properties. The results obtained from XRD show a hexagonal zincite shape and crystallite sizes in the range of 22.25–31.05 nm. The appearance of a signal at ∼400 cm−1 obtained from FTIR confirms the formation of the Zn-O- bond. Subsequently, the removal of different organic dyes from polluted water was analyzed using zinc oxide semiconductor nanoparticles as photocatalysts under ultraviolet light. The results show outstanding degradation of the dyes, being able to remove at least 98.0 %, 84.4 %, 94.64 %, 95.5 %, and 98.2 % for methylene blue, methyl orange, rhodamine-B, Congo red, and malachite green, respectively. Additionally, the antibacterial effect of the obtained materials against multiple pathogenic bacteria was studied. All the synthesized nanoparticle samples showed an antibacterial effect, even at low concentrations for all the analyzed pathogens. The results show the feasibility of using Bauhinia forficata to obtain zinc oxide nanoparticles and its multiple applications due to its improved properties.
In recent times, the continuous growth of construction and demolition (C&D) activities have resulted in increases in the utilization of natural resources as well as global C&D waste production. A major part of C&D waste produced is dumped in landfills worldwide although some countries have adopted good recycling and reuse facilities to generated C&D waste. Based on an extensive critical review of published literature on the topic including global C&D waste recycling statistics and composition of generated wastes, this paper identifies key physical, mechanical, and geotechnical characteristics of recycled C&D waste aggregates specific to use as pavement base or subbase materials. Recycled aggregates typically have sufficient CBR, abrasion resistance, compressive strength and resilient modulus in accordance with various road standard specifications, which enable their applications for pavement base and subbase layer construction. Recycled aggregates typically have higher water absorption and lower specific gravity values than virgin aggregates. Furthermore, this study evaluates the feasibility and effectiveness of recycled aggregates in pavement base and subbase layers based on the detailed laboratory investigations. Additionally, case studies involving large-volume utilization of recycled aggregates for field-scale pavement construction are presented facilitating the broader adoption of recycled materials in sustainable construction of road pavements. These studies document crucial insights into its real field performance in terms of strength, durability and longevity. Finally, authors have discussed the potential challenges, research gaps and future insights on the use of recycled aggregates in pavement construction. The use of recycled aggregates in pavement construction still has some barriers and challenges such as availability in bulk quantity especially at the field scale and absence of road standards for application, which require further research and practical developments to promote the sustainable use of these materials in the future.
Concrete manufacturing is highly resource-intensive and is a major source of greenhouse gas emission. Accelerating depletion of natural resources such as sand, which is the primary material for aggregate in concrete manufacture is a growing problem. At the same time, the disposal of vast volumes of non-biodegradable plastic waste poses a global environmental challenge. The incorporation of aggregates derived from municipal plastic waste to substitute for sand has the potential to help address both issues, while at the same time mitigating greenhouse gas emission. This study examines the potential of municipal polyethylene terephthalate (PET) plastic waste as a fine aggregate in concrete manufacturing. The primary focus was on PET aggregates with non-uniform and uniform shapes ranging in size from 2.36 to 4.75 mm. In the concrete mixtures, 0 %, 30 %, and 50 % of the fine natural aggregate by volume were replaced with fine PET aggregate with a water to cement ratio of 0.40. The obtained results showed a reduction in compressive and splitting tensile strength when compared to control specimens. However, replacing 30 % of fine natural aggregate with PET (both uniform and non-uniform shapes) significantly improved chloride resistance by 13 % and 12 %, respectively, while also enhancing the bond between cement paste and PET particles. This study characterizes the material properties of PET concrete, which represents a promising method for reusing municipal plastic waste and mitigating environmental concerns in concrete production.
Recycled coarse aggregate concrete enables the creation of environmentally friendly and cost-effective mixes. It helps address the disposal problem of demolition concrete waste, meeting demand while improving product functionality and reusability. The abundance of obsolete buildings in cemeteries contributes to Construction and Demolition waste. Recycled Concrete Aggregate (RCA) from demolished structures can be utilized as aggregates, albeit with concerns about its impact on compressive strength due to absorption issues. This review aimed to study and develop the different Artificial Intelligence (AI) model for the prediction of the compressive strength of concrete with varying RCA content and natural coarse aggregate content as input parameters while compressive strength as output parameter. The range of the input parameters is 0 % to 100 % while the range output parameter is 28 MPa to 70.3 MPa. Experimental data from literature articles used to train and validate the model development. Engineers and researchers can utilize these models to predict compressive strength by changing the input parameters. XGBoost Regression Model performed well with R2 0.93594 followed by Random Forest Model with R2 0.92766, and Gradient Boosting Model with R2 0.90616 respectively. Ridge Regression, Lasso Regression, and Linear Regression Models were not performed well in predicting the compressive strength of RCA concrete with R2 0.57657, 0.57558, 0.57675 respectively. ANN also performed significant in prediction of RCAC compressive strength with R2 0.8039. Future research could focus on optimizing the mechanical properties of concrete containing RCA using AI models. Furthermore, the study extends its analysis to explore the application of AI in predicting the strength of various types of concrete, highlighting the versatility and potential of AI-driven approaches in enhancing concrete mix design.