Chemical Engineering Transactions (Dec 2023)

Response Surface Methodology Design and Optimization of Inorganic Phosphate Removal from Simulated Wastewater Effluent Utilizing Caulerpa lentillifera Algal Powder

  • Tristan Roy L. Panaligan,
  • Jared Andrei N. Pagal,
  • Sharwin Jae J. Cancisio

DOI
https://doi.org/10.3303/CET23106011
Journal volume & issue
Vol. 106

Abstract

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The excessive amount of phosphate is a critical issue in several countries since this leads to eutrophication that may result in algal bloom. Biosorption processes are currently gaining recognition to remove wastewater contaminants since the sorbent source can either be alive or dead, which, for the latter can be beneficial in terms of end-of-life utilization as well as cost. Response surface methodology (RSM) design was used to evaluate the effect of pH, contact time, initial phosphate concentration, and biosorbent dosage in the percent removal of inorganic phosphate from simulated wastewater with an alga (Caulerpa lentillifera) as the biological raw material. This method of experimental design concentrated on using a specific window for each parameter and obtaining the most optimal combination of these parameters by comparing their predicted and actual responses to better understand their interactive effects. The raw seaweed was subjected to drying and size reduction for it to become powder form. This algal powder used was characterized by Fourier Transform Infrared Spectroscopy to determine the functional groups present. The highest percent removal obtained in the experiment proper was 42.29 % on average. The data from the experiment proper was assessed by making use of a statistical analysis software, JMP® (SAS institute), and showcased about 45.46 % as the predicted optimum percent phosphate removal. Running the test based on the best parameter combination and comparing the actual percentage removal resulting from the validation provided only a 3.89 % difference to the predicted value of the software. Analysis of variance (ANOVA) was applied to the RSM results, and the predicted R2 value, as well as that of the adjusted R2 was found to have good interaction with each other with the difference between the values much less than 0.2.