Chitosan based grey wastewater treatment--a statistical design approach

Area of Science:

• Environmental Science
• Chemical Engineering
• Water Treatment Technologies

Background:

• Grey wastewater poses environmental challenges due to pollutants like turbidity, BOD, and COD.
• Effective treatment methods are crucial for water resource management and pollution control.

Purpose of the Study:

• To optimize the grey wastewater treatment process using chitosan.
• To investigate the impact of agitation time, pH, chitosan dose, and settling time on treatment efficiency.
• To develop predictive models for turbidity, BOD, and COD removal.

Main Methods:

• Response Surface Methodology (RSM) with a Box-Behnken Design (BBD).
• Four factors were investigated: agitation time (1-3 min), pH (2.5-5.5), chitosan dose (0.3-0.6 g/L), and settling time (10-20 min).
• Analysis of Variance (ANOVA) and second-order polynomial models were used for analysis and prediction.

Main Results:

• Optimal conditions yielded high removal efficiencies: 96% for turbidity, 91% for BOD, and 73% for COD.
• RSM successfully predicted the treatment responses.
• Pareto analysis identified key process variables influencing removal rates.

Conclusions:

• Chitosan treatment, optimized via RSM, is effective for grey wastewater purification.
• The developed models provide a reliable tool for predicting treatment outcomes.
• This study offers a data-driven approach to enhance wastewater treatment efficiency.