Component analysis and comprehensive evaluation of biomethane systems

doi:10.11949/0438-1157.20201928

Abstract

Abstract:

Biomethane system can produce methane from organic wastes, which has developed rapidly in the past twenty years. However, biomethane system has some disadvantages, such as complex raw material source, low utilization rate of organic matter, high output of biogas slurry and biogas residue. Therefore, it is very important to clarify and evaluate the composition of raw materials, biogas slurry and biogas residue during the process of biomethane system, and then optimize the biomethane system and reduce pollutant emissions. In present study, the biomethane system of typical raw materials was established. The component of raw materials, biogas, biogas slurry and biogas residue were analyzed. Moreover, the comprehensive evaluation system of biomethane system with green degree, methane yield, contamination degree of heavy metals and potential ecological risk was constructed. The advantages and disadvantages of the biomethane system with five different raw materials were obtained. The green degree and methane yield were the highest in the biomethane system of swine manure with 0.222 and 212 ml·g^-1, respectively, whereas the contamination degree of heavy metals of biogas residue was the highest. The green degree was 0.200 in the biomethane system of cattle manure, and the contamination degree of heavy metals of biogas residue was the lowest, but the methane yield was only 116 ml·g^-1. The potential ecological risk and contamination degree of heavy metals of biogas slurry were the lowest in the biomethane system of straw, whereas the green degree and methane yield was low. The methane yield was 183 ml·g^-1 in the biomethane system of chicken manure, however the contamination degree of heavy metals and potential ecological risk of biogas slurry were the highest, and the green degree was low. The biomethane system of food waste raw materials has the worst performance, the lowest green degree and methane yield, and the highest potential ecological risk of biogas residue.

Key words: biomethane system, raw materials, slurry and residue, component analysis, evaluation

摘要：

明确生物甲烷过程中原料、沼液和沼渣的组成与系统的综合评价，在优化生物甲烷系统、减少污染物排放等方面都具有重要意义。研究了牛粪、猪粪、秸秆、餐厨垃圾和鸡粪等五种典型原料的生物甲烷系统，分析原料、沼气、沼液和沼渣的组成，并在此基础上构建了包含绿色度、甲烷产率、沼液沼渣的重金属污染度和潜在生态风险四个方面的生物甲烷系统综合评价体系。研究结果表明：猪粪原料的生物甲烷系统绿色度和甲烷产率最高，分别达到0.222和212 ml·g^-1，但其沼渣的重金属污染度最高；牛粪原料的生物甲烷系统绿色度较高，达到0.200，沼渣的重金属污染度最低，但甲烷产率仅有116 ml·g^-1；秸秆原料的生物甲烷系统沼渣的潜在生态风险最低，沼液的重金属污染度和潜在生态风险较低，但绿色度和甲烷产率偏低；鸡粪原料的生物甲烷系统甲烷产率较高，达到183 ml·g^-1，但沼液的重金属污染度和潜在生态风险都最高，且绿色度较低；餐厨垃圾原料的生物甲烷系统性能最差，绿色度和甲烷产率最低，并且沼渣的潜在生态风险最高。

关键词: 生物甲烷系统, 原料, 沼液和沼渣, 组分分析, 评价

CLC Number:

S 216.4

KE Lanting, WANG Yuanpeng, ZHENG Yanmei, LI Qingbiao. Component analysis and comprehensive evaluation of biomethane systems[J]. CIESC Journal, 2021, 72(7): 3801-3813.

柯蓝婷, 王远鹏, 郑艳梅, 李清彪. 生物甲烷系统的组分分析与综合评价[J]. 化工学报, 2021, 72(7): 3801-3813.

Figures/Tables 12

Fig.1 Diagrammatic drawing of experimental equipment

Table 1 Toxic response factors and background reference level of heavy metals

重金属	$T r i$	$C R i$ /（沼液）(μg·L^-1)	$C R i$ /（沼渣^[35]）(mg·kg^-1)
As	10	10	15
Cd	30	5	1.0
Cr	2	50	90
Cu	5	1000	50
Hg	40	1	0.25
Pb	5	10	70
Zn	1	1000	175

Table 1 Toxic response factors and background reference level of heavy metals

重金属	$T r i$	$C R i$ /（沼液）(μg·L^-1)	$C R i$ /（沼渣^[35]）(mg·kg^-1)
As	10	10	15
Cd	30	5	1.0
Cr	2	50	90
Cu	5	1000	50
Hg	40	1	0.25
Pb	5	10	70
Zn	1	1000	175

Table 2 Background reference level of component in biomethane system

组分	$c i R$ （沼液）/(mg·L^-1)	$c i R$ （沼渣）/(mg·kg^-1)
As	0.5	40
Cd	0.1	1.0
Cr	1.5	300
Cu	2	400
Hg	0.05	1.5
Pb	1	500
Zn	5	500
COD	500	—
NH₄⁺-N	25	—
PO₄^3-	1	—

Table 2 Background reference level of component in biomethane system

组分	$c i R$ （沼液）/(mg·L^-1)	$c i R$ （沼渣）/(mg·kg^-1)
As	0.5	40
Cd	0.1	1.0
Cr	1.5	300
Cu	2	400
Hg	0.05	1.5
Pb	1	500
Zn	5	500
COD	500	—
NH₄⁺-N	25	—
PO₄^3-	1	—

Table 3 Basic parameters of biomethane systems from five substrates

运行参数	牛粪	猪粪	秸秆	餐厨垃圾	鸡粪
原料质量/g	22.30	24.81	5.20	22.19	23.32
TS含量/%	28.14	25.01	92.03	20.67	25.16
VS含量/%	17.94	16.12	76.85	18.03	17.15
初始TS质量/g	6.28	6.20	4.79	4.59	5.87
初始VS质量/g	4.00	4.00	4.00	4.00	4.00
反应后TS质量/g	3.51	3.03	2.82	2.69	3.05
TS移除率/%	44.11	51.13	41.13	41.39	48.04
反应体积/L	0.20	0.20	0.20	0.20	0.20

Fig.2 Biogas and methane yield of biomethane systems from five substrates

Fig.3 COD (a), polysaccharide (b), NH4+-N (c) and PO43- (d) concentration of slurry from biomethane systems of five substrates

Fig.4 Solute, hemicellulose, cellulose, lignin and ash content of raw material and residue from biomethane systems of five substrates

Fig.5 C content (a), N content (b), S content (c), H content (d), C∶N (e) and C∶H (f) of raw material and residue from biomethane systems of five substrates

Table 4 Heavy metals content of biomethane systems from five substrates

重金属种类	浓度分布	牛粪	猪粪	秸秆	餐厨垃圾	鸡粪
As	原料浓度/(mg·kg^-1)	0.516±0.061	0.773±0.088	0.412±0.056	0.498±0.084	0.731±0.013
	沼液浓度/(μg·L^-1)	3.460±0.358	9.739±0.401	3.904±0.258	3.848±0.100	16.336±0.996
	沼渣浓度/(mg·kg^-1)	0.617±0.056	0.713±0.077	0.541±0.023	1.003±0.091	0.263±0.027
Cd	原料浓度/(mg·kg^-1)	0.135±0.004	0.287±0.066	0.239±0..018	0.094±0.008	0.201±0.009
	沼液浓度/(μg·L^-1)	0.590±0.029	0.332±0.050	0.162±0.017	0.559±0.057	1.106±0.098
	沼渣浓度/(mg·kg^-1)	0.421±0.050	0.278±0.008	0.376±0.051	0.288±0.062	0.478±0.010
Cr	原料浓度/(mg·kg^-1)	5.240±0.098	10.329±0.935	3.049±0.122	2.926±0.435	1.020±0.313
	沼液浓度/(μg·L^-1)	26.466±4.773	15.157±0.589	17.052±0.846	27.126±0.939	54.288±5.010
	沼渣浓度/(mg·kg^-1)	3.528±0.077	6.546±0.883	6.927±0.601	2.104±0.624	3.550±0.269
Cu	原料浓度/(mg·kg^-1)	25.427±0.925	65.325±6.034	36.164±2.090	15.185±0.751	52.814±5.689
	沼液浓度/(μg·L^-1)	102.554±8.971	59.666±6.817	28.726±2.851	45.549±2.013	178.156±8.678
	沼渣浓度/(mg·kg^-1)	64.140±6.192	110.277±8.713	71.441±6.191	24.857±0.844	78.480±4.647
Hg	原料浓度/(mg·kg^-1)	0.124±0.019	0.059±0.002	0.048±0.003	0.327±0.057	0.028±0.002
	沼液浓度/(μg·L^-1)	0.236±0.008	0.004±0.000	0.193±0.015	0.117±0.007	0.506±0.041
	沼渣浓度/(mg·kg^-1)	0.179±0.009	0.146±0.014	0.030±0.002	0.835±0.053	0.228±0.015
Pb	原料浓度/(mg·kg^-1)	0.519±0.061	0.647±0.084	0.419±0.041	0.248±0.012	0.211±0.040
	沼液浓度/(μg·L^-1)	3.420±0.074	1.090±0.082	1.736±0.093	1.311±0.082	1.999±0.080
	沼渣浓度/(mg·kg^-1)	1.094±0.053	0.987±0.069	0.734±0.031	0.411±0.034	0.500±0.039
Zn	原料浓度/(mg·kg^-1)	173.933±9.156	284.267±18.677	238.230±19.560	54.942±4.733	212.408±11.427
	沼液浓度/(μg·L^-1)	410.492±30.889	172.310±5.965	154.432±9.269	246.271±8.343	497.088±28.334
	沼渣浓度/(mg·kg^-1)	246.751±14.917	500.338±47.739	463.365±13.419	118.443±7.612	357.921±20.907

Table 4 Heavy metals content of biomethane systems from five substrates

重金属种类	浓度分布	牛粪	猪粪	秸秆	餐厨垃圾	鸡粪
As	原料浓度/(mg·kg^-1)	0.516±0.061	0.773±0.088	0.412±0.056	0.498±0.084	0.731±0.013
	沼液浓度/(μg·L^-1)	3.460±0.358	9.739±0.401	3.904±0.258	3.848±0.100	16.336±0.996
	沼渣浓度/(mg·kg^-1)	0.617±0.056	0.713±0.077	0.541±0.023	1.003±0.091	0.263±0.027
Cd	原料浓度/(mg·kg^-1)	0.135±0.004	0.287±0.066	0.239±0..018	0.094±0.008	0.201±0.009
	沼液浓度/(μg·L^-1)	0.590±0.029	0.332±0.050	0.162±0.017	0.559±0.057	1.106±0.098
	沼渣浓度/(mg·kg^-1)	0.421±0.050	0.278±0.008	0.376±0.051	0.288±0.062	0.478±0.010
Cr	原料浓度/(mg·kg^-1)	5.240±0.098	10.329±0.935	3.049±0.122	2.926±0.435	1.020±0.313
	沼液浓度/(μg·L^-1)	26.466±4.773	15.157±0.589	17.052±0.846	27.126±0.939	54.288±5.010
	沼渣浓度/(mg·kg^-1)	3.528±0.077	6.546±0.883	6.927±0.601	2.104±0.624	3.550±0.269
Cu	原料浓度/(mg·kg^-1)	25.427±0.925	65.325±6.034	36.164±2.090	15.185±0.751	52.814±5.689
	沼液浓度/(μg·L^-1)	102.554±8.971	59.666±6.817	28.726±2.851	45.549±2.013	178.156±8.678
	沼渣浓度/(mg·kg^-1)	64.140±6.192	110.277±8.713	71.441±6.191	24.857±0.844	78.480±4.647
Hg	原料浓度/(mg·kg^-1)	0.124±0.019	0.059±0.002	0.048±0.003	0.327±0.057	0.028±0.002
	沼液浓度/(μg·L^-1)	0.236±0.008	0.004±0.000	0.193±0.015	0.117±0.007	0.506±0.041
	沼渣浓度/(mg·kg^-1)	0.179±0.009	0.146±0.014	0.030±0.002	0.835±0.053	0.228±0.015
Pb	原料浓度/(mg·kg^-1)	0.519±0.061	0.647±0.084	0.419±0.041	0.248±0.012	0.211±0.040
	沼液浓度/(μg·L^-1)	3.420±0.074	1.090±0.082	1.736±0.093	1.311±0.082	1.999±0.080
	沼渣浓度/(mg·kg^-1)	1.094±0.053	0.987±0.069	0.734±0.031	0.411±0.034	0.500±0.039
Zn	原料浓度/(mg·kg^-1)	173.933±9.156	284.267±18.677	238.230±19.560	54.942±4.733	212.408±11.427
	沼液浓度/(μg·L^-1)	410.492±30.889	172.310±5.965	154.432±9.269	246.271±8.343	497.088±28.334
	沼渣浓度/(mg·kg^-1)	246.751±14.917	500.338±47.739	463.365±13.419	118.443±7.612	357.921±20.907

Fig.6 C (a), N (b) and P (c) mass balance of biomethane systems from five substrates

Fig.7 Green degree (a), methane yield (b), contamination degree (c) and risk indices (d) of biomethane systems from five substrates

Table 5 Green degree of biomethane systems from five substrates

Raw materials	$G D k 1 s, i n$		$G D k 2 e$	$G D k 3 s, o u t$					$Δ G D u$
	EUP	ECP		GWP	EUP		ECP
	EUP	ECP		GWP	Slurry	Residue	Slurry	Residue
CAM	-0.24382	-0.16872	-0.00741	-0.00287	-0.04395	-0.05687	-0.00036	-0.10063	0.20044
SM	-0.20177	-0.28099	-0.00603	-0.00436	-0.07127	-0.05886	-0.00021	-0.12024	0.22180
St	-0.11954	-0.16757	-0.00713	-0.00359	-0.05011	-0.05375	-0.00015	-0.10172	0.07066
KW	-0.15366	-0.09018	-0.00805	-0.00256	-0.16480	-0.04585	-0.00023	-0.07413	-0.05178
CHM	-0.24622	-0.19075	-0.00644	-0.00404	-0.15710	-0.05133	-0.00059	-0.11157	0.10590

Table 5 Green degree of biomethane systems from five substrates

Raw materials	$G D k 1 s, i n$		$G D k 2 e$	$G D k 3 s, o u t$					$Δ G D u$
	EUP	ECP		GWP	EUP		ECP
	EUP	ECP		GWP	Slurry	Residue	Slurry	Residue
CAM	-0.24382	-0.16872	-0.00741	-0.00287	-0.04395	-0.05687	-0.00036	-0.10063	0.20044
SM	-0.20177	-0.28099	-0.00603	-0.00436	-0.07127	-0.05886	-0.00021	-0.12024	0.22180
St	-0.11954	-0.16757	-0.00713	-0.00359	-0.05011	-0.05375	-0.00015	-0.10172	0.07066
KW	-0.15366	-0.09018	-0.00805	-0.00256	-0.16480	-0.04585	-0.00023	-0.07413	-0.05178
CHM	-0.24622	-0.19075	-0.00644	-0.00404	-0.15710	-0.05133	-0.00059	-0.11157	0.10590

References 44

1	Patinvoh R J, Taherzadeh M J. Challenges of biogas implementation in developing countries[J]. Current Opinion in Environmental Science & Health, 2019, 12: 30-37.
2	Ferreira S F, Buller L S, Berni M, et al. Environmental impact assessment of end-uses of biomethane[J]. Journal of Cleaner Production, 2019, 230: 613-621.
3	Xiao Y Q, Yang H N, Yang H, et al. Improved biogas production of dry anaerobic digestion of swine manure[J]. Bioresource Technology, 2019, 294: 122188.
4	Gu Y, Chen X H, Liu Z G, et al. Effect of inoculum sources on the anaerobic digestion of rice straw[J]. Bioresource Technology, 2014, 158: 149-155.
5	Zhang T, Yang Y H, Xie D T. Insights into the production potential and trends of China's rural biogas[J]. International Journal of Energy Research, 2015, 39(8): 1068-1082.
6	Scheutz C, Fredenslund A M. Total methane emission rates and losses from 23 biogas plants[J]. Waste Management, 2019, 97: 38-46.
7	Wang S L, Jena U, Das K C. Biomethane production potential of slaughterhouse waste in the United States[J]. Energy Conversion and Management, 2018, 173: 143-157.
8	Scarlat N, Fahl F, Dallemand J F, et al. A spatial analysis of biogas potential from manure in Europe[J]. Renewable and Sustainable Energy Reviews, 2018, 94: 915-930.
9	Holm-Nielsen J B, Al Seadi T, Oleskowicz-Popiel P. The future of anaerobic digestion and biogas utilization[J]. Bioresource Technology, 2009, 100(22): 5478-5484.
10	Baena-Moreno F M, Malico I, Rodríguez-Galán M, et al. The importance of governmental incentives for small biomethane plants in South Spain[J]. Energy, 2020, 206: 118158.
11	Cucchiella F, D'Adamo I, Gastaldi M. An economic analysis of biogas-biomethane chain from animal residues in Italy[J]. Journal of Cleaner Production, 2019, 230: 888-897.
12	Giwa A S, Ali N, Ahmad I, et al. Prospects of China's biogas: fundamentals, challenges and considerations[J]. Energy Reports, 2020, 6: 2973-2987.
13	北极星环保网. 2017年沼气发电市场前景分析: 2020年总量将达440亿立方米 [EB/OL]. [2020-11-29]..
	Beijixing Environmental Protection Network. Biogas generation prospect analysis: the biogas production will reach 44 billion m³by 2020 [EB/OL]. [2020-11-29]..
14	Li K, Liu R H, Sun C. Comparison of anaerobic digestion characteristics and kinetics of four livestock manures with different substrate concentrations[J]. Bioresource Technology, 2015, 198: 133-140.
15	Dai X H, Hua Y, Liu R, et al. Biomethane production by typical straw anaerobic digestion: deep insights of material compositions and surface properties[J]. Bioresource Technology, 2020, 313: 123643.
16	Mao C L, Feng Y Z, Wang X J, et al. Review on research achievements of biogas from anaerobic digestion[J]. Renewable and Sustainable Energy Reviews, 2015, 45: 540-555.
17	Li H, Chen Z, Fu D, et al. Improved ADM1 for modelling C, N, P fates in anaerobic digestion process of pig manure and optimization approaches to biogas production[J]. Renewable Energy, 2020, 146: 2330-2336.
18	武斌, 张香平, 许亚晶, 等. 生物甲烷系统技术评价与集成的研究进展[J]. 化工进展, 2014, 33(7): 1659-1670.
	Wu B, Zhang X P, Xu Y J, et al. Progress of evaluation and integration of biomethane system[J]. Chemical Industry and Engineering Progress, 2014, 33(7): 1659-1670.
19	Mahanty B, Zafar M, Han M J, et al. Optimization of co-digestion of various industrial sludges for biogas production and sludge treatment: methane production potential experiments and modeling[J]. Waste Management, 2014, 34(6): 1018-1024.
20	Zhang T, Liu L, Song Z, et al. Biogas production by co-digestion of goat manure with three crop residues[J]. PLoS One, 2013, 8(6): e66845.
21	Fernández-Rodríguez J, Pérez M, Romero L I. Comparison of mesophilic and thermophilic dry anaerobic digestion of OFMSW: kinetic analysis[J]. Chemical Engineering Journal, 2013, 232: 59-64.
22	Perez M, Romero L I, Sales D. Organic matter degradation kinetics in an anaerobic thermophilic fluidised bed bioreactor[J]. Anaerobe, 2001, 7(1): 25-35.
23	Li Y Q, Zhang R H, Chen C, et al. Biogas production from co-digestion of corn stover and chicken manure under anaerobic wet, hemi-solid, and solid state conditions[J]. Bioresource Technology, 2013, 149: 406-412.
24	Akbulut A, Kose R, Akbulut A. Technical and economic assessments of biogas production in a family size digester utilizing different feedstock rotations: Döğer case study[J]. International Journal of Green Energy, 2014, 11(2): 113-128.
25	Nasr N, Elbeshbishy E, Hafez H, et al. Comparative assessment of single-stage and two-stage anaerobic digestion for the treatment of thin stillage[J]. Bioresource Technology, 2012, 111: 122-126.
26	Li H L, Lindmark J, Nordlander E, et al. Using the solid digestate from a wet anaerobic digestion process as an energy resource[J]. Energy Technology, 2013, 1(1): 94-101.
27	Zheng X R, Liu Y Q, Huang J M, et al. The influence of variables on the bioavailability of heavy metals during the anaerobic digestion of swine manure[J]. Ecotoxicology and Environmental Safety, 2020, 195: 110457.
28	Kuo W C, Sneve M A, Parkin G F. Formation of soluble microbial products during anaerobic treatment[J]. Water Environment Research, 1996, 68(3): 279-285.
29	Wang Y J, Feng L S, Zhao X S, et al. Characteristics of volatile compounds removal in biogas slurry of pig manure by ozone oxidation and organic solvents extraction[J]. Journal of Environmental Sciences, 2013, 25(9): 1800-1807.
30	Chen W L, Lin S C, Huang C H, et al. Wide-scope screening for pharmaceutically active substances in a leafy vegetable cultivated under biogas slurry irrigation[J]. Science of the Total Environment, 2021, 750: 141519.
31	李祎雯, 曲英华, 徐奕琳, 等. 不同发酵原料沼液的养分含量及变化[J]. 中国沼气, 2012, 30(3): 17-20, 24.
	Li Y W, Qu Y H, Xu Y L, et al. Change of nutrition contents of biogas slurry with different fermentation raw materials[J]. China Biogas, 2012, 30(3): 17-20, 24.
32	靳红梅, 常志州, 叶小梅, 等. 江苏省大型沼气工程沼液理化特性分析[J]. 农业工程学报, 2011, 27(1): 291-296.
	Jin H M, Chang Z Z, Ye X M, et al. Physical and chemical characteristics of anaerobically digested slurry from large-scale biogas project in Jiangsu Province[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(1): 291-296.
33	柯蓝婷, 王海涛, 王远鹏, 等. 不同来源家庭户用沼气池沼液成分分析及风险评价[J]. 化工学报, 2014, 65(5): 1840-1847.
	Ke L T, Wang H T, Wang Y P, et al. Component analysis and risk assessment of anaerobically digested slurry from households in China[J]. CIESC Journal, 2014, 65(5): 1840-1847.
34	国家环境保护总局. 水和废水监测分析方法[M]. 4版. 北京: 中国环境出版社, 2002.
	State Environmental Protection Administration. Water and Wastewater Monitoring and Analysis Methods[M]. 4th ed. Beijing: China Environmental Science Press, 2002.
35	Hakanson L. An ecological risk index for aquatic pollution control: a sedimentological approach[J]. Water Research, 1980, 14(8): 975-1001.
36	Zhang X P, Li C S, Fu C, et al. Environmental impact assessment of chemical process using the green degree method[J]. Industrial & Engineering Chemistry Research, 2008, 47(4): 1085-1094.
37	Yan R Y, Li Z X, Diao Y Y, et al. Green process for methacrolein separation with ionic liquids in the production of methyl methacrylate[J]. AIChE Journal, 2011, 57(9): 2388-2396.
38	付超, 张香平, 闫瑞一, 等. 绿色度方法中环境影响因子权值的确定[J]. 计算机与应用化学, 2008, 25(9): 1068-1074.
	Fu C, Zhang X P, Yan R Y, et al. Weighting factors of environmental impact categories in green degree method[J]. Computers and Applied Chemistry, 2008, 25(9): 1068-1074.
39	邢赜. 沼液营养物的沸石吸附回收与利用[D]. 重庆: 西南大学, 2013.
	Xing Z. Adsorption and its utilization of nutrient from biogas slurry by zeolite[D]. Chongqing: Southwest University, 2013.
40	李恒, 柯蓝婷, 王海涛, 等. 低劣生物质厌氧产甲烷过程的模拟研究进展[J]. 化工学报, 2014, 65(5): 1577-1586.
	Li H, Ke L T, Wang H T, et al. Simulation research on anaerobic digestion biogas generation from low-grade biomass[J]. CIESC Journal, 2014, 65(5): 1577-1586.
41	Labatut R A, Angenent L T, Scott N R. Biochemical methane potential and biodegradability of complex organic substrates[J]. Bioresource Technology, 2011, 102(3): 2255-2264.
42	Triolo J M, Sommer S G, Møller H B, et al. A new algorithm to characterize biodegradability of biomass during anaerobic digestion: influence of lignin concentration on methane production potential[J]. Bioresource Technology, 2011, 102(20): 9395-9402.
43	Li W W, Khalid H, Amin F R, et al. Biomethane production characteristics, kinetic analysis, and energy potential of different paper wastes in anaerobic digestion[J]. Renewable Energy, 2020, 157: 1081-1088.
44	Li H, Tan F, Ke L T, et al. Mass balances and distributions of C, N, and P in the anaerobic digestion of different substrates and relationships between products and substrates[J]. Chemical Engineering Journal, 2016, 287: 329-336.

[1]	Yue CAO, Chong YU, Zhi LI, Minglei YANG. Industrial data driven transition state detection with multi-mode switching of a hydrocracking unit [J]. CIESC Journal, 2023, 74(9): 3841-3854.
[2]	Guang WANG, Fashun SHAN, Yucheng QIAN, Jianfang JIAO. Incipient fault detection method for chemical process based on ensemble learning transfer entropy [J]. CIESC Journal, 2023, 74(7): 2967-2978.
[3]	Yurong DANG, Chunlan MO, Kerui SHI, Yingcong FANG, Ziyang ZHANG, Zuoshun LI. Comprehensive evaluation model combined with genetic algorithm for the study on the performance of ORC system with zeotropic mixture [J]. CIESC Journal, 2023, 74(5): 1884-1895.
[4]	Jinyu GUO, Zhe WANG, Yuan LI. Fault detection method based on kernel entropy independent component analysis [J]. CIESC Journal, 2022, 73(8): 3647-3658.
[5]	Libang LIU, Song YANG, Zhijian WANG, Xinxin HE, Wenlei ZHAO, Shoujun LIU, Wenguang DU, Jie MI. Prediction of coke quality based on improved WOA-LSTM [J]. CIESC Journal, 2022, 73(3): 1291-1299.
[6]	Cheng ZHANG, Lizhi PAN, Yuan LI. Fault detection and diagnosis method based on weighted statistical feature KICA [J]. CIESC Journal, 2022, 73(2): 827-837.
[7]	Zhenlin ZHU, Songlin WANG, Bingxue JIANG, Jiaxu LI, Wei DENG, Haiqiang WU, Xuan YANG, Pingwei LIU, Wenjun WANG. Study on biodegradation of polyesters and their evaluation methods [J]. CIESC Journal, 2022, 73(1): 110-121.
[8]	Jinyu GUO, Wentao LI, Yuan LI. Application of adaptive algorithm of online reduced KECA in fault detection [J]. CIESC Journal, 2021, 72(8): 4227-4238.
[9]	JIA Xiaoping, SHI Lei, YANG Youqi. Challenges of eco-industrial parks development and opportunities for process systems engineering [J]. CIESC Journal, 2021, 72(5): 2373-2391.
[10]	ZHU Xiongzhuo, ZHANG Hanwen, YANG Chunjie. MWPCA blast furnace anomaly monitoring algorithm based on Gaussian mixture model [J]. CIESC Journal, 2021, 72(3): 1539-1548.
[11]	LI Yuan, YANG Dongsheng, ZHAO Liying, ZHANG Cheng. Fault detection using hierarchical variational Gaussian mixture model and principal polynomial analysis [J]. CIESC Journal, 2021, 72(3): 1616-1626.
[12]	Zhenzhen YE, Xinqi CHEN, Jian WANG, Bofan LI, Chaojie CUI, Gang ZHANG, Luming QIAN, Ying JIN, Weizhong QIAN. Evaluation of aging performance under high temperature of ionic liquid-based pouch supercapacitor [J]. CIESC Journal, 2021, 72(12): 6351-6360.
[13]	Huimin YUN, Jianjun DAI, Hui LI, Xiaotao BI. Economic and environmental assessment of biomass coupled coal-fired power generation [J]. CIESC Journal, 2021, 72(12): 6311-6327.
[14]	Xiufeng LIU, Shi ZHANG, Zhijie ZHOU, Hao ZHENG, Chengze WANG, Hongyuan SHI, Mengjie LI. Study on structure optimization of heat exchanger and evaluation index of heat transfer performance [J]. CIESC Journal, 2020, 71(S1): 98-105.
[15]	Mingyue DENG, Jianchang LIU, Peng XU, Shubin TAN, Liangliang SHANG. New fault detection and diagnosis strategy for nonlinear industrial process based on KECA [J]. CIESC Journal, 2020, 71(5): 2151-2163.