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Elucidating co-conditioning strategies of aluminium-based sludge using natural biopolymeric magnetite composite for leveraging dewaterability
Applied Water Science ( IF 5.5 ) Pub Date : 2024-04-25 , DOI: 10.1007/s13201-024-02167-6
Zahraa A. Elsayed , Ibrahim E. El-Sayed , Maha A. Tony

Significant co-conditioning of aluminum-based sludge using alternative conditioner (natural biopolymer-magnetite/H2O2) as a Fenton conditioner for improving the sludge dewaterability is investigated in the current study. Firstly, natural biopolymer/magnetite (NBP-M) were prepared in various ratios and the samples labeled as (NBP-M-(1:1); NBP-M-(1:3) and NBP-M-(2:1). Focuses were placed on factors influencing such novel Fenton conditioner. Capillary suction time (CST) was utilized to assess sludge dewaterability. Experiments have exhibited that significant development of alum sludge dewaterability could be attained at minimum reaction time and using low concentrations of the applied catalyst, i.e. natural biopolymer/magnetite and H2O2. It was found that sludge conditioning and dewaterability became better with increasing both reagents concentrations. A Box–Behnken experimental design based on the response surface methodology was investigated to explore the optimum of the influencing variables, i.e. catalyst (NBP-M) concentration, H2O2 loading and pH. The optimized values for NBP-M, H2O2, and pH are 46 and 380 mg/L, respectively at pH 3.0, at which the CST reduction efficiency of 58 ± 3% can be attained, this approved with that predicted by an established polynomial model in the current investigation.



中文翻译:

阐明使用天然生物聚合磁铁矿复合材料的铝基污泥的协同调节策略以利用脱水性

本研究研究了使用替代调节剂(天然生物聚合物-磁铁矿/H 2 O 2 )作为芬顿调节剂对铝基污泥进行显着共调节,以提高污泥脱水性。首先,以不同比例制备天然生物聚合物/磁铁矿(NBP-M),并将样品标记为(NBP-M-(1:1);NBP-M-(1:3)和NBP-M-(2:1) )重点关注影响这种新型芬顿调节剂的因素,用于评估污泥脱水性。实验表明,在最短的反应时间内和使用低浓度的明矾污泥脱水性可以得到显着的提高。催化剂,即天然生物聚合物/磁铁矿和H 2 O 2。发现随着两种试剂浓度的增加,污泥调理和脱水性变得更好,研究了基于响应面方法的Box-Behnken实验设计,以探索影响的最佳值。变量,即催化剂 (NBP-M) 浓度、H 2 O 2负载量和pH 在 pH 3.0 时, NBP-M、H 2 O 2和 pH的优化值分别为 46 和 380 mg/L。可以实现 58 ± 3% 的 CST 降低效率,这与当前研究中建立的多项式模型的预测相一致。

更新日期:2024-04-25
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