In this study, the adsorptive removal of two dyes (crystal violet (CV) and methylene blue (MB)) with HNO3 pre-treated water hyacinth powder (WHP) adsorbent was analysed. The experiments were designed using response surface methodology (RSM) with variable input parameter pH (2-12), adsorbent dose (0.5-3 g/L), initial dyes concentration (25-200 mg/L) and time (10-180 min). The optimization condition for dye removal were (pH = 7.22, adsorbent dose = 3.0 g/L, initial dye concentration = 195.28 mg/L and time of contact = 99.29 min) for CV with removal of 98.20% and (pH = 9.82, adsorbent dose = 2.96 g/L, initial dye concentration = 199.36 mg/L and contact time = 111.74 min) for MB with removal of 97.843%. The above findings observed that pre-treated water hyacinth powder can be utilised as a cost-effective and efficient adsorbent for dye effluent wastewater treatment.

This study aims to examine the ability of free floating aquatic plants to remove phosphorus and to predict the reduction of phosphorus from rice mill wastewater using soft computing techniques. A mesocosm study was conducted at the mill premises under normal conditions, and reliable results were obtained. Four aquatic plants, namely water hyacinth, water lettuce, salvinia, and duckweed were used for this study. The growth of all the plants was inhibited in rice mill wastewater due to low pH, high chemical oxygen demand, high conductivity, and high phosphorus concentration. Subsequently, a 1:1 ratio of mill water to tap water was used. A control was maintained to assess the aquatic plant technology. In this study, the aquatic plants reduced the total phosphorus content up to 80 % within 15 days. A comparison between three modeling techniques e.g. Artificial neural network (ANN), Random forest (RF) and M5P has been done considering the reduction rate of total phosphorus as predicted variable. In this paper, the data set has been divided in two parts, 70 % is used to train the model and residual 30 % is used for testing of the model. Artificial neural network shows promising results as compared to random forest and M5P tree modelling. The root mean square error (RMSE) for all the three models is observed as 0.0162, 0.0204 and 0.0492 for ANN, RF and M5P tree, respectively.

Phytoremediation is an eco-friendly method for removal of pollutants, which can be relied upon as a sustainable technology, if implemented under optimum conditions of plant growth. The effectiveness of water hyacinth, a topical weed, for the removal of Zinc (Zn) and Chromium (Cr) ions from aqueous solutions has been presented in this article. The potential of this plant in removing metals by phytoremediation was explored under various environmental factors such as pH, salinity, metal concentrations, available nutrients, and so on. The efficiency of metal removal was observed by varying the different parameters. It was found that the maximum removal of metals occurred at a neutral pH, low amount of salinity, lower metal ion concentrations, and lack of nutrients. The stress induced in a plant by metal absorption was visible from the health and growth pattern of the plants. The stress on water hyacinth due to metals was also assessed, by observing the changes in its chlorophyll and protein content.