The blooming of the world’s human population and the transition of the human diet into a more westernized, high-protein diet has accelerated the production of slaughterhouse wastewater (SWW) as the number of meat processing plants (MPP) has increased in the past few decades. Conventional treatment processes (CTP) used in treating SWW, such as anaerobic processes, membrane processes, and electrocoagulation, have significant limitations, such as low treatment efficiency, tendency to foul, and high energy consumption, respectively. While advanced oxidation processes (AOPs) appear promising in replacing the former, they lack economic feasibility when used as a single process. In this paper, the limitations and disadvantages of the CTPs used in treating SWW influents are evaluated. The idea of utilising AOPs as a “complementary” step rather than a single process is also discussed. The review paper further explores the variability of different AOPs, such as Fenton, Electro-Fenton, Sono-Fenton, etc., and their respective strengths and weaknesses in counteracting the limitations of CTPs. The idea of incorporating resource recovery into wastewater treatment is also discussed towards the end of the paper as a means of generating additional revenue for the industry players to compensate for the high operation and maintenance costs of SWW treatment. The integration of a new-generation treatment process such as AOP into CTP while being able to carry out resource recovery is a future hurdle that must be overcome by scientists in order to produce a versatile, powerful, sustainable, yet financially feasible and operationally pragmatic treatment system.

An artificial neural network (ANN) model was developed to simulate the biodegradation of herbicide glyphosate [2-(Phosphonomethylamino) acetic acid] in a solution with varying parameters pH, inoculum size and initial glyphosate concentration. The predictive ability of ANN model was also compared with Monod model. The result showed that ANN model was able to accurately predict the experimental results. A low ratio of self-inhibition and half saturation constants of Haldane equations (< 8) exhibited the inhibitory effect of glyphosate on bacteria growth. The value of K_i/K_s increased when the mixed inoculum size was increased from 10⁴ to 10⁶ bacteria/mL. It was found that the percentage of glyphosate degradation reached a maximum value of 99% at an optimum pH 6-7 while for pH values higher than 9 or lower than 4, no degradation was observed.

This study aims to produce amidoxime-modified poly(acrylonitrile-co-acrylic acid) using an optimized method and to investigate the performance of amidoxime-modified poly(acrylonitrile-co-acrylic acid) on the adsorption of boron ions in batch operations. Batch adsorption was conducted at the physiochemical parameters of pH, adsorbent dosage, and initial boron concentration. The isotherms and kinetics of adsorption data were studied at various initial boron concentrations. The renewed synthesis process gave a production yield of 77%, and the conversion of nitrile group to amidoxime was 78%. The adsorption reached its optimum point at pH = 8, adsorbent dosage = 4 g·L−1, and initial adsorbent concentration at 40 ppm. The best model fits for isotherm adsorption was the Sips model with heterogeneity factor (n) = 0.7611. In the kinetic study, the adsorption data fitted best with pseudo-second-order model. The synthesized polymeric adsorbent could be recycled with little decline in its boron entrapment capacities. Hence, amidoxime-modified poly(acrylonitrile-co-acrylic acid) exhibited high adsorption capacity and could be potentially explored as an alternative to commercial resin in the removal of boron from wastewater.

This paper studied the removal of the herbicide Glyphosate in aqueous solution using ions capable of forming an insoluble complex Glyphosate salt (ferric chloride). A maximum Glyphosate removal of 67.4% was achieved using $300\mathrm{mg}/\mathrm{L}$ of ferric chloride. The precipitation of the insoluble Glyphosate salt was affected by rapid mixing velocity, rapid mixing time, dosage of flocculant, and types of impellers. An artificial neural networks (ANN) model was used to predict the removal of Glyphosate. The results showed good agreement over the range of experimental and predicted data. Increasing the velocity gradient increased the Glyphosate removal. The Glyphosate removal decreased with further increase in shear stress. At higher flocculant dosage, the effect of impeller shear is less as the flocs are stronger. The results showed that the effect of impeller type was highly dependent on the rapid mixing velocity and rapid mixing time.