流化床中煤气化细渣高温还原磷石膏过程

doi:10.16085/j.issn.1000-6613.2023-0883

摘要/Abstract

摘要：

磷石膏（PG）是世界上重要的工业固体废物之一，其传统的储存方法占用了大片土地，也破坏了环境。采用热化学方法将PG还原为CaS和CaO不仅可以变废为宝，而且缓解了环境污染。常见的还原剂主要是褐煤和硫黄等，然而上述还原剂存在成本较高的缺点。因此本文提出采用煤气化细渣（CGFS）来还原PG。通过热力学计算、流化床实验和动力学计算，探讨了还原行为和反应机理。首先，热力学计算表明CGFS还原PG完全可行。流化床实验发现当目标产物为CaS时，最优化的反应条件为温度应保持在850~900℃，C/Ca摩尔比为2~3，在该条件下，PG可以完全分解。当目标产物为CaO时，温度应保持在950~1000℃，C/Ca摩尔比为0.5~1，但在该条件下PG难以完全分解。此外CGFS中的矿物组分会显著影响PG的分解率，该过程主要是和CGFS的反应活性有关。接着通过比较四种常见的碳基还原剂发现，褐煤对PG的分解效率最高，细渣和焦炭相比于石墨具有较高的反应活性，也有利于PG的分解过程。此外提高C/Ca摩尔比和反应温度能够降低石墨与其他三种还原剂的差距。最后动力学研究表明，CGFS还原PG过程符合缩核反应模型，其动力学机理函数G(α)=-ln(1-α)，表观活化能为415.78~456.83kJ/mol。通过SEM-EDS发现反应是从边界开始，逐渐扩散到核心，最终形成蜂窝结构。本研究将为开发环境友好型还原剂分解PG提供理论依据。

关键词: 磷石膏, 煤气化细渣, 动力学, 热力学, 分解机理

Abstract:

Phosphogypsum (PG) is one of the important industrial solid waste in the world, and its traditional storage method occupies a large area of land and damages the environment. The reduction of PG to CaS and CaO by thermochemical methods not only turns waste into treasure, but also alleviates environmental pollution. The common reducing agents are mainly lignite and sulfur, etc. However, the above reducing agents have the disadvantage of high cost. Therefore, this paper proposed to use coal gasification fine slag (CGFS) to reduce PG. The reduction behavior and reaction mechanism were explored by thermodynamic calculations, fluidized bed experiments and kinetic calculations. First, thermodynamic calculations showed that the reduction of PG by CGFS was fully feasible. The fluidized bed experiments revealed that when the target product was CaS, the optimized reaction conditions were that the temperature should be kept at 850—900℃ and the C/Ca molar ratio was 2—3, under which the PG can be completely decomposed. When the target product was CaO, the temperature should be kept at 950—1000℃ and the C/Ca molar ratio was 0.5—1, but it was difficult to decompose the PG completely under this condition. In addition, the mineral fraction in CGFS can significantly affect the decomposition rate of PG and the process was mainly related to the reactivity of CGFS. Then, by comparing four common carbon-based reducing agents, it was found that lignite had the highest decomposition efficiency for PG and the fine slag and coke had higher reactivity compared to graphite, which also facilitated the decomposition process of PG. In addition, increasing the C/Ca molar ratio and reaction temperature was able to reduce the gap between graphite and the other three reducing agents. Finally, the kinetic study of the CGFS reduction process of PG indicated that the reduction process was consistent with the shrinkage nucleation reaction model with the kinetic mechanism function G(α)=-ln(1-α) and apparent activation energy of 415.78—456.83kJ/mol. It was found by SEM-EDS that the reaction started from the boundary, gradually diffused to the core and finally formed a honeycomb structure. This study would provide a theoretical basis for the development of environmentally friendly reductant decomposition of PG.

Key words: phosphogypsum, coal gasification fine slag, kinetics, thermodynamics, decomposition mechanism

中图分类号:

TQ177.375

马栋, 解桂林, 田治华, 王勤辉, 张建国, 宋慧林, 钟晋. 流化床中煤气化细渣高温还原磷石膏过程[J]. 化工进展, 2024, 43(6): 3479-3491.

MA Dong, XIE Guilin, TIAN Zhihua, WANG Qinhui, ZHANG Jianguo, SONG Huilin, ZHONG Jin. Analysis of high temperature reduction process of phosphogypsum by coal gasification fine slag in fluidized bed[J]. Chemical Industry and Engineering Progress, 2024, 43(6): 3479-3491.

图/表 14

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样品	工业分析				元素分析
样品	M_ad	A_ad	V_ad	FC_ad	C	H	O^①	N	S_t
脱灰前	0.45	67.62	1.96	29.97	26.64	0.38	4.43	0.12	0.36
脱灰后	3.24	4.86	12.37	79.53	75.69	3.24	11.71	0.31	0.95

样品	工业分析				元素分析
样品	M_ad	A_ad	V_ad	FC_ad	C	H	O^①	N	S_t
脱灰前	0.45	67.62	1.96	29.97	26.64	0.38	4.43	0.12	0.36
脱灰后	3.24	4.86	12.37	79.53	75.69	3.24	11.71	0.31	0.95

反应模型	代号	G (α)	f (α)
相界控制的反应（一维）	R1	α	1
相界控制的反应（二维）	R2	1-(1-α)^1/2	2(1-α)^1/2
相界控制的反应（三维）	R3	1-(1-α)^1/3	3(1-α)^2/3
能量最低模型	P2/3	α^3/2	2/3α^-1/2
能量最低模型	P2	α^1/2	2α^1/2
能量最低模型	P3	α^1/3	3α^2/3
一维扩散模型	D1	α²	1/(2α)
二维扩散模型	D2	(1-α)ln(1-α)+α	[-ln(1-α)]^-1
三维扩散模型	D3	[1-（1-α）^1/3]²	(3/2)(1-α)^2/3[1-(1-α)^1/3]^-1
缩核模型 (n=1)	A1	-ln(1-α)	1-α
缩核模型(n=1/2)	A2	[-ln(1-α)]^1/2	2(1-α)[-ln(1-α)]^1/2
缩核模型(n=1/3)	A3	[-ln(1-α)]^1/3	3(1-α)[-ln(1-α)]^2/3
缩核模型(n=1/4)	A4	[-ln(1-α)]^1/4	4(1-α)[-ln(1-α)]^3/4
缩核模型(n=2/3)	A5	[-ln(1-α)]^2/3	3/2(1-α)[-ln(1-α)]^1/3
缩核模型(n=2)	A6	[-ln(1-α)]²	0.5(1-α)[-ln(1-α)]^-1
缩核模型(n =1.5)	A1.5	[-ln(1-α)]^3/2	2/3(1-α)[-ln(1-α)]^-0.5

反应模型	代号	G (α)	f (α)
相界控制的反应（一维）	R1	α	1
相界控制的反应（二维）	R2	1-(1-α)^1/2	2(1-α)^1/2
相界控制的反应（三维）	R3	1-(1-α)^1/3	3(1-α)^2/3
能量最低模型	P2/3	α^3/2	2/3α^-1/2
能量最低模型	P2	α^1/2	2α^1/2
能量最低模型	P3	α^1/3	3α^2/3
一维扩散模型	D1	α²	1/(2α)
二维扩散模型	D2	(1-α)ln(1-α)+α	[-ln(1-α)]^-1
三维扩散模型	D3	[1-（1-α）^1/3]²	(3/2)(1-α)^2/3[1-(1-α)^1/3]^-1
缩核模型 (n=1)	A1	-ln(1-α)	1-α
缩核模型(n=1/2)	A2	[-ln(1-α)]^1/2	2(1-α)[-ln(1-α)]^1/2
缩核模型(n=1/3)	A3	[-ln(1-α)]^1/3	3(1-α)[-ln(1-α)]^2/3
缩核模型(n=1/4)	A4	[-ln(1-α)]^1/4	4(1-α)[-ln(1-α)]^3/4
缩核模型(n=2/3)	A5	[-ln(1-α)]^2/3	3/2(1-α)[-ln(1-α)]^1/3
缩核模型(n=2)	A6	[-ln(1-α)]²	0.5(1-α)[-ln(1-α)]^-1
缩核模型(n =1.5)	A1.5	[-ln(1-α)]^3/2	2/3(1-α)[-ln(1-α)]^-0.5

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