多种废水制备水煤浆智能化专家系统的设计及应用

doi:10.16085/j.issn.1000-6613.2019-1365

化工进展 ›› 2020, Vol. 39 ›› Issue (5): 1701-1706.DOI: 10.16085/j.issn.1000-6613.2019-1365

多种废水制备水煤浆智能化专家系统的设计及应用

刘建忠(), 王双妮, 李得第, 王金乾

浙江大学能源清洁利用国家重点实验室，浙江杭州 310027

出版日期:2020-05-05 发布日期:2020-05-25
通讯作者: 刘建忠
作者简介:刘建忠（1965—），男，博士，教授，主要从事煤高效清洁燃烧与转化、燃烧源污染物形成与控制、新能源与可再生能源等方面的研究。E-mail：jzliu@zju.edu.cn。
基金资助:
国家重点研发计划(2016YFB0600505)

Design and application of intelligent expert system for preparation of coal water slurry by using various wastewaters

Jianzhong LIU(), Shuangni WANG, Dedi LI, Jinqian WANG

State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310029, Zhejiang, China

Online:2020-05-05 Published:2020-05-25
Contact: Jianzhong LIU

摘要/Abstract

摘要：

有机废水组分繁多、种类各异，国内发展成熟的水煤浆技术能高效、低成本、资源化处理煤化工等行业的高浓度有机废水。目前针对多种废水制备水煤浆少有研究，如何以最优化的配比进行多种废水制浆，是十分有意义的课题。为了解决这一问题，本文围绕高浓度有机废水制备水煤浆的影响研究，将废水中的不同组分作为影响水煤浆性能的多因子，采用神经网络技术，设计了一套能够求解最佳废水配比、预测废水煤浆成浆浓度的专家系统。此专家系统能够满足企业生产的要求，提高企业的经济效益。为了验证专家系统预测成浆浓度的准确性，进行了14组不同废水比例的成浆性实验，并将实际实验结果与预测结果进行对比，得到预测值与测试值的误差均小于10%，具有较高的可信度。

关键词: 专家系统, 废水, 浆料, 成浆浓度, 神经网络

Abstract:

The organic wastewater contains various components. The well-developed coal-water slurry technology in China can treat high-concentration organic wastewater in coal chemical industry with high efficiency, low cost and resources utilization. At present, there are few studies on the preparation of coal-water slurry from various wastewaters. How to make coal-water slurry from various wastewaters with the optimum proportion is a very meaningful subject. In order to solve this problem, the influence of high-concentration organic wastewater on the performance of coal water slurry was studied. By using neural network technology, a set of expert system was designed, which can solve the optimal wastewater ratio and predict the concentration of wastewater coal slurry. This expert system can meet the requirements of enterprise production and improve the economic efficiency of enterprise. To verify the accuracy of expert system in predicting slurry concentration, 14 groups of experiments with different proportion of wastewater were carried out, and the actual experimental results were compared with the predicted results. The errors between the predicted values and the measured values are less than 10%, which have high reliability.

Key words: expert system, wastewater, slurry, slurry concentration, neural networks

中图分类号:

TQ536

刘建忠, 王双妮, 李得第, 王金乾. 多种废水制备水煤浆智能化专家系统的设计及应用[J]. 化工进展, 2020, 39(5): 1701-1706.

Jianzhong LIU, Shuangni WANG, Dedi LI, Jinqian WANG. Design and application of intelligent expert system for preparation of coal water slurry by using various wastewaters[J]. Chemical Industry and Engineering Progress, 2020, 39(5): 1701-1706.

参考文献

1	HAN L H, ZHU J, KANG J, et al. Catalytic wet air oxidation of high-strength organic coking wastewater[J]. Asia-Pacific Journal of Chemical Engineering, 2009, 4(5): 624-627.
2	LI D D, LIU J Z, WANG J Q, et al. Experimental studies on coal water slurries prepared from coal gasification wastewater[J]. Asia‐Pacific Journal of Chemical Engineering, 2018, 13: e2162.
3	SUN W L, QU Y Z, YU Q, et al. Adsorption of organic pollutants from coking and papermaking wastewaters by bottom ash[J]. Journal of Hazardous Materials, 2008, 154(1/2/3): 595-601.
4	ZHUANG H F, HAN H J, HOU B L, et al. Heterogeneous catalytic ozonation of biologically pretreated Lurgi coal gasification wastewater using sewage sludge based activated carbon supported manganese and ferric oxides as catalysts[J]. Bioresource Technology, 2014, 166: 178-186.
5	汪逸. 有机废水及其复杂组分对水煤浆添加剂性能影响的机理研究[D]. 杭州: 浙江大学, 2019.WANG Y. Research on influence mechanism of organic wastewater and its complex components on additives of coal water slurry [D]. Hangzhou: Zhejiang University, 2019.
6	闵凡飞, 张明旭. 工业废水水煤浆性能的研究[J]. 安徽理工大学学报(自然科学版), 2000, 20(4): 50-55.
6	MIN F F, ZHANG M X. Study on features of coal water slurry with industrial waste water[J]. Journal of Huainan Institute of Technology (Natural Science), 2000, 20(4): 50-55.
7	郑福尔, 刘以凡, 刘明华. 利用高浓度印染废水制备水煤浆的研究[J]. 煤炭工程, 2012, 1(2): 85-87.
7	ZHENG F E, LIU Y F, LIU M H. Study on high density dyeing and printing waste water applied to preparation of coal water slurry[J]. Coal Engineering, 2012, 1(2): 85-87.
8	SHAO S, CHEN X G, LIU H F, et al. Preparation of coal slurry with alcohol fermentation wastewater[J]. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2012, 34(10): 919-928.
9	汪逸, 刘建忠, 李宁, 等. 煤气化废水成浆特性及添加剂的适配性[J]. 化工进展, 2018, 37(8): 3206-3213.
9	WANG Y, LIU J Z, LI N, et al. Slurryability of coal gasification wastewater and adaptation of additives[J]. Chemical Industry and Engineering Progress, 2018, 37(8): 3206-3213.
10	ZHANG J, ZHAO H, WANG C Y, et al. Influence of small oxygen-containing organic molecules on the rheology of the coal water slurry[J]. Energy & Fuels, 2016, 30(7): 5506-5512.
11	WANG J Q, LIU J Z, WANG S N, et al. Slurrying property and mechanism of coal-coal gasification wastewater-slurry[J]. Energy & Fuels, 2018, 32(4): 4833-4840.
12	MAKAROV A S, BORUK S D, EGURNOV A I, et al. Utilization of industrial wastewater in production of coal-water fuel[J]. Journal of Water Chemistry and Technology, 2014, 36(4): 180-183.
13	LIU M G, DUAN Y F, BIKANE K, et al. Effect of waste liquid produced from the hydrothermal treatment of both low-rank coal and sludge on the slurryability of coal sludge slurry[J]. Fuel, 2017, 203: 1-10.
14	WANG S N, WU J Q, LIU J Z, et al. Effect of ammonia nitrogen and low-molecular-weight organics on the adsorption of additives on coal surface: a combination of experiments and molecular dynamics simulations[J]. Chemical Engineering Science, 2019, 205: 134-142.
15	LIU J Z, WANG S N, LI N, et al. Effects of metal ions in organic wastewater on coal water slurry and dispersant properties[J]. Energy & Fuels, 2019, 33(8): 7110-7117.
16	李婷. 气化用煤配煤模型及动态配煤系统研究[D]. 西安: 西安科技大学, 2017.LI T. Research on the model and system of dynamic coal blending for gasification process[D]. Xi’an: Xi’an University of Science and Technology, 2017.
17	胡亚轩. 含碳固体燃料混合成浆特性及配煤成浆浓度神经网络预测[D]. 杭州: 浙江大学, 2015.HU Y X. Characteristics of slurryability of carbon-fuel and solid concentration prediction of blending-CWS by neural networks[D]. Hangzhou: Zhejiang University, 2015.

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多种废水制备水煤浆智能化专家系统的设计及应用

Design and application of intelligent expert system for preparation of coal water slurry by using various wastewaters

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