Modeling and optimization of lime-based stabilization in high alkaline arsenic-bearing sludges with a central composite design

ABSTRACT

This study focuses on the modeling and optimization of lime-based stabilization in high alkaline arsenic-bearing sludges (HAABS) and describes the relationship between the arsenic leachate concentration (ALC) and stabilization parameters to develop a prediction model for obtaining the optimal process parameters and conditions. A central composite design (CCD) along with response surface methodology (RSM) was conducted to model and investigate the stabilization process with three independent variables: the Ca/As mole ratio, reaction time and liquid/solid ratio, along with their interactions. The obvious characteristic changes of the HAABS before and after stabilization were verified by X-ray diffraction (XRD), scanning electron microscopy (SEM), particle size distribution (PSD) and the community bureau of reference (BCR) sequential extraction procedure. A prediction model Y_(ALC) with a statistically significant P-value <0.01 and high correlation coefficient R² = 93.22% was obtained. The optimal parameters were successfully predicted by the model for the minimum ALC of 0.312 mg/L, which was validated with the experimental result (0.306 mg/L). The XRD, SEM and PSD results indicated that crystal calcium arsenate Ca₅(AsO₄)₃OH and Ca₄(OH)₂(AsO₄)₂·4H₂O formation played an important role in minimizing the ALC. The BCR sequential extraction results demonstrated that the treated HAABS were stable in a weak acidic environment for a short time but posed a potential environmental risk after a long time. The results clearly confirm that the proposed three-factor CCD is an effective approach for modeling the stabilization of HAABS. However, further solidification technology is suggested for use after lime-based stabilization treatment of arsenic-bearing sludges.

KEYWORDS:

• Arsenic-bearing sludges
• BCR sequential extraction
• calcium arsenate crystallization
• central composite design
• optimization
• response surface methodology
• stabilization

Funding

The authors gratefully acknowledge the Special Program on Environmental Protection for Public Welfare (201509050), the National Natural Science Foundation of China (51634010 and 51474247), the Key Project of Science and Technology of Hunan Province (2014FJ1011), grants from the Project of Innovation-Driven Plan in Central South University (No. 2015CX001), and the Co-Innovation Center for Clean and Efficient Utilization of Strategic Metal Mineral Resources for the financial support of this study.