Zhihua Chen
Huazhong University of Science and Technology
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Featured researches published by Zhihua Chen.
Bioresource Technology | 2015
Zhihua Chen; Mian Hu; Xiaolei Zhu; Dabin Guo; Shiming Liu; Zhiquan Hu; Bo Xiao; Jingbo Wang; Mahmood Laghari
Pyrolysis characteristics and kinetic of five lignocellulosic biomass pine wood sawdust, fern (Dicranopteris linearis) stem, wheat stalk, sugarcane bagasse and jute (Corchorus capsularis) stick were investigated using thermogravimetric analysis. The pyrolysis of five lignocellulosic biomass could be divided into three stages, which correspond to the pyrolysis of hemicellulose, cellulose and lignin, respectively. Single Gaussian activation energy distributions of each stage are 148.50-201.13 kJ/mol with standard deviations of 2.60-13.37 kJ/mol. The kinetic parameters of different stages were used as initial guess values for three-parallel-DAEM model calculation with good fitting quality and fast convergence rate. The mean activation energy ranges of hemicellulose, cellulose and lignin were 148.12-164.56 kJ/mol, 171.04-179.54 kJ/mol and 175.71-201.60 kJ/mol, with standard deviations of 3.91-9.89, 0.29-1.34 and 23.22-27.24 kJ/mol, respectively. The mass fractions of hemicellulose, cellulose and lignin in lignocellulosic biomass were respectively estimated as 0.12-0.22, 0.54-0.65 and 0.17-0.29.
Bioresource Technology | 2015
Mian Hu; Zhihua Chen; Dabin Guo; Cuixia Liu; Bo Xiao; Zhiquan Hu; Shiming Liu
The pyrolysis process of two microalgae, Chlorella pyrenoidosa (CP) and bloom-forming cyanobacteria (CB) was examined by thermo-gravimetry to investigate their thermal decomposition behavior under non-isothermal conditions. It has found that the pyrolysis of both microalgae consists of three stages and stage II is the major mass reduction stage with mass loss of 70.69% for CP and 64.43% for CB, respectively. The pyrolysis kinetics of both microalgae was further studied using single-step global model (SSGM) and distributed activation energy model (DAEM). The mean apparent activation energy of CP and CB in SSGM was calculated as 143.71 and 173.46 kJ/mol, respectively. However, SSGM was not suitable for modeling pyrolysis kinetic of both microalgae due to the mechanism change during conversion. The DAEM with 200 first-order reactions showed an excellent fit between simulated data and experimental results.
Bioresource Technology | 2016
Xun Wang; Mian Hu; Wanyong Hu; Zhihua Chen; Shiming Liu; Zhiquan Hu; Bo Xiao
Pyrolytic kinetic of an agricultural residue (AR) feedstock, a mixture of plants (cotton, wheat, rich, corn) stems, was investigated based on combined kinetics. The most suitable mechanism for AR one-step pyrolysis was f(α)=(1-α)(1.1816)α(-1.8428) with kinetic parameters of: apparent activation energy 221.7kJ/mol, pre-exponential factor 4.17E16s(-1). Pyrolysis of AR feedstock could not be described by one-step reaction attributes to heterogeneous features of pyrolysis processes. Combined kinetics three-parallel-reaction (CK-TPR) model fitted the pyrolysis experimental data very well. Reaction mechanisms for pseudo hemicelluloses, cellulose, lignin in CK-TPR model was f(α)=(1-α)(1.6244)α(-0.3371)[-ln(1-α)](-0.0515), f(α)=(1-α)(1.0597)α(-0.6909)[-ln(1-α)](0.9026) and f(α)=(1-α)(2.9577)α(-4.7719), respectively. Apparent activation energy of three pseudo components followed the order of Elignin(197.3kJ/mol)>Ecellulose(176.3kJ/mol)>Ehemicelluloses (151.1kJ/mol). Mechanism of hemicelluloses pyrolysis could be further expressed as f(α)=(1-α)(1.4). The pyrolytic mechanism of cellulose met the Nucleation well. However, mechanism of lignin pyrolysis was complex, which possibly was the combined effects of Nucleation, Diffusion, Geometrical contraction, and Power law.
Bioresource Technology | 2015
Zhiquan Hu; Zhihua Chen; Genbao Li; Xiaojuan Chen; Mian Hu; Mahmood Laghari; Xun Wang; Dabin Guo
The pyrolysis characteristics and kinetic of Hydrilla verticillata (HV) have been investigated using non-isothermal thermogravimetric analysis. The results showed that the pyrolysis behavior of HV can be divided into two independent stages. The kinetics of Stage I was investigated using a distributed activation energy model (DAEM) with discrete 99 first-order reactions. Stage II was an independent stage which corresponds to the decomposition of calcium oxalate, whose kinetics was studied using iso-conversional method together with compensation effect and master-plots method. The activation energies ranged from 92.39 to 506.17 and 190.42 to 222.48 kJ/mol for the first and second stages respectively. Calculated data gave very good fit to the experimental data.
Journal of the Science of Food and Agriculture | 2016
Mahmood Laghari; Ravi Naidu; Bo Xiao; Zhiquan Hu; Muhammad Saffar Mirjat; Mian Hu; Muhammad Nawaz Kandhro; Zhihua Chen; Dabin Guo; Qamardudin Jogi; Zaidun Naji Abudi; Saima Fazal
In recent years biochar has been demonstrated to be a useful amendment to sequester carbon and reduce greenhouse gas emission from the soil to the atmosphere. Hence it can help to mitigate global environment change. Some studies have shown that biochar addition to agricultural soils increases crop production. The mechanisms involved are: increased soil aeration and water-holding capacity, enhanced microbial activity and plant nutrient status in soil, and alteration of some important soil chemical properties. This review provides an in-depth consideration of the production, characterization and agricultural use of different biochars. Biochar is a complex organic material and its characteristics vary with production conditions and the feedstock used. The agronomic benefits of biochar solely depend upon the use of particular types of biochar with proper field application rate under appropriate soil types and conditions.
Waste Management | 2016
Zhihua Chen; Mian Hu; Baihui Cui; Shiming Liu; Dabin Guo; Bo Xiao
The effects of bioleaching on sewage sludge pyrolysis were studied. Sewage sludge was treated by bioleaching with solid concentrations of 6% (w/v), 8% (w/v), 10% (w/v). Results showed that bioleaching treatment could modify the physicochemical properties of sewage sludge and enhance the metals removal. The optimum removal efficiencies of heavy metals were achieved with solid concentration of 6% (w/v) bioleaching treatment: Cu, 73.08%; Zn, 78.67%; Pb, 24.65%; Cd, 79.46%. The characterization results of thermogravimetric analysis (TGA) showed that the bioleached sewage sludge with a 6% (w/v) solid concentration treatment was the easiest to decompose. Pyrolytic experiments of bioleached sewage sludge were performed in a laboratory-scale fixed bed reactor. Results indicated that bioleaching treatment greatly influenced the product yields and gas composition.
Bioresource Technology | 2016
Xiaojuan Chen; Zhihua Chen; Xun Wang; Chan Huo; Zhiquan Hu; Bo Xiao; Mian Hu
The present study focused on the application of anaerobic digestion model no. 1 (ADM1) to simulate biogas production from Hydrilla verticillata. Model simulation was carried out by implementing ADM1 in AQUASIM 2.0 software. Sensitivity analysis was used to select the most sensitive parameters for estimation using the absolute-relative sensitivity function. Among all the kinetic parameters, disintegration constant (kdis), hydrolysis constant of protein (khyd_pr), Monod maximum specific substrate uptake rate (km_aa, km_ac, km_h2) and half-saturation constants (Ks_aa, Ks_ac) affect biogas production significantly, which were optimized by fitting of the model equations to the data obtained from batch experiments. The ADM1 model after parameter estimation was able to well predict the experimental results of daily biogas production and biogas composition. The simulation results of evolution of organic acids, bacteria concentrations and inhibition effects also helped to get insight into the reaction mechanisms.
Journal of Dispersion Science and Technology | 2017
Ping Yang; Dabin Guo; Zhihua Chen; Baihui Cui; Bo Xiao; Shiming Liu; Mian Hu
ABSTRACT Magnetic biochar, as an adsorbent, was synthesized by a single step method, where iron salt was directly mixed with pinewood sawdust by chemical co-precipitation and subsequently pyrolyzed at 700°C for Cr (VI) removal from aqueous solution. The effects of some important parameters including adsorbent dosage (0.4–2.8 g/L), pH (1–10) of the solution, contact time (0–1440 minutes), initial concentration (30–120 mg/L), and temperature (20–40°C) were investigated in batch experiments. Both pre- and post-adsorbents were characterized by SEM-EDX and XPS to investigate the adsorption mechanism. The maximum adsorption capacity of the tested magnetic biochar under the certain experimental conditions determined as optimal was 42.7 mg/g for Cr (VI). The adsorption data were proved to be suitable for the pseudo-second order model for kinetics and the Langmuir model for isotherms with correlation R2 = 0.9996 andR2 > 0.9980, respectively, after fitting with four kinetic models (pseudo-first order, pseudo-second order, W-M model, and Elovich) and three isotherm models (Langmuir, Freundlich, and Temkin). The characteristic analyses further verified that the efficient particle was a mixture of iron oxides in essence, and it had a strong effect on the spontaneous and endothermic adsorption process. GRAPHICAL ABSTRACT
Energy Sources Part A-recovery Utilization and Environmental Effects | 2016
Qunpeng Cheng; Meng Jiang; Zhihua Chen; Xun Wang; Bo Xiao
ABSTRACT Pyrolysis and kinetic behavior of banana stem (BS) were investigated using thermogravimetric analysis (TGA). The behavior of mass loss demonstrated that pyrolysis process of BS appeared in three stages with a conversion range of 0–0.20, 0.20–0.90, and 0.90–1, respectively. The reaction mechanism of BS pyrolysis followed the 3D diffusion model, with an apparent activation energy range of 130.63–192.10 kJ/mol and a pre-exponential factor range of 2.42×107–4.10 × 1010 s–1. Stages 1, 2, and 3 were mainly attributed to pyrolysis of hemicellulose, cellulose, and lignin with mean activation energies of 139.09, 155.41, and 188.71 kJ/mol, respectively. The experimental data obeyed the iso-conversional model well with correlation coefficients (R2) over than 0.9928.
Energy Conversion and Management | 2016
Mian Hu; Zhihua Chen; Shengkai Wang; Dabin Guo; Caifeng Ma; Yan Zhou; Jian Chen; Mahmood Laghari; Saima Fazal; Bo Xiao; Beiping Zhang; Shu Ma