Modified Orange Peel Waste as a Sustainable Material for Adsorption of Contaminants.

World orange production is estimated at 60 million tons per annum, while the annual production of orange peel waste is 32 million tons. According to available data, the adsorption capacity of orange peel ranges from 3 mg/g to 5 mg/g, while their water uptake is lower than 1 mg/g. The low water uptake of orange peel and the abundance of biomass in nature has made orange peel an excellent biosorption material. This review summarised different studies on orange peel adsorption of various contaminants to identify properties of orange peel that influence the adsorption of contaminants. Most of the literature reviewed studied orange peel adsorption of heavy metals, followed by studies on the adsorption of dyes, while few studies have investigated adsorption of oil by orange peel. FTIR spectra analysis and SEM micrographs of raw and activated orange peels were studied to understand the structural properties of the biomass responsible for adsorption. The study identified pectin, hydroxyl, carbonyl, carboxyl, and amine groups as components and important functional groups responsible for adsorption in orange peel. Furthermore, changes were observed in the structural properties of the peel after undergoing various modifications. Physical modification increased the surface area for binding and the adsorption of contaminants, while chemical treatments increased the carboxylic groups enhancing adsorption and the binding of contaminants. In addition, heating orange peel during the thermal modification process resulted in a highly porous structure and a subsequent increase in adsorption capacities. In conclusion, physical, chemical, and thermal treatments improve the structural properties of orange peel, resulting in high biosorption uptake. However, orange peels treated with chemicals recorded the highest contaminants adsorption capacities.

Wet-Dry Cycles and Microstructural Characteristics of Expansive Subgrade Treated with Sustainable Cementitious Waste Materials.

This work presents an experimental study on the physico-mechanical and microstructural characteristics of stabilised soils and the effect of wetting and drying cycles on their durability as road subgrade materials. The durability of expansive road subgrade with a high plasticity index treated with different ratios of ground granulated blast furnace slag (GGBS) and brick dust waste (BDW) was investigated. Treated and cured samples of the expansive subgrade were subjected to wetting-drying cycles, California bearing ratio (CBR) tests, and microstructural analysis. The results show a gradual reduction in the California bearing ratio (CBR), mass, and the resilient modulus of samples for all subgrade types as the number of cycles increases. The treated subgrades containing 23.5% GGBS recorded the highest CBR value of 230% under dry conditions while the lowest CBR value of 15% (wetting cycle) was recorded for the subgrade treated with 11.75% GGBS and 11.75% BDW at the end of the wetting-drying cycles, both of which find useful application in road pavement construction as calcium silicate hydrate (CSH) gel was formed in all stabilised subgrade materials. However, the increase in alumina and silica content upon the inclusion of BDW initiated the formation of more cementitious products due to the increased availability of Si and Al species as indicated by EDX analysis. This study concluded that subgrade materials treated with a combination of GGBS and BDW are durable, sustainable and suitable for use in road construction.

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans-A Machine Learning Approach.

The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans. Both ML regression and multinomial classification of the UCS of the stabilised mix are investigated. Rigorous sensitivity-driven diagnostic testing is also performed to validate and provide an understanding of the intricacies of the decisions made by the algorithm. Results indicate that the well-tuned and optimised GB algorithm has a very high capacity to distinguish between positive and negative UCS categories ('firm', 'very stiff' and 'hard'). An overall accuracy of 0.920, weighted recall rates and precision scores of 0.920 and 0.938, respectively, were produced by the GB model. Multiclass prediction in this regard shows that only 12.5% of misclassified instances was achieved. When applied to a regression problem, a coefficient of determination of approximately 0.900 and a mean error of about 0.335 were obtained, thus lending further credence to the high performance of the GB algorithm used. Finally, among the eight input features utilised as independent variables, the additives seemed to exhibit the strongest influence on the ML predictive modelling.