RENEWABLE ENERGY
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2021
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IF 8.001
Gul Muhammad,Ange Douglas Potchamyou Ngatcha,Yongkun Lv,Wenlong Xiong,Yaser A. El-Badry,Eylem Asmatulu,Jingliang Xu,Md Asraful Alam
This study investigates modeling and optimal conditions for biodiesel production from exceedingly wet microalgae Chlorella pyrenoidosa using the catalyst, hydrochloric acid . Three levels of Box-Behnken design response surface methodology were used to optimize individual and interactive effects of parameter time (120–240 min), temperature (120–160 °C), solvent-to-wet biomass ratio (2.0–4.67), and hydrochloric acid concentration (2–4 M). Temperature was the most significant factor for direct transesterification of wet microalgae (low p-value (0.0001) and high F-value (53.89). The highest yield (19.90%) of fatty acid methyl ester was obtained on dry biomass weight basis under the optimum conditions of 240 min, 146 °C, 2.83 (vol/wt), and 3.86 M acid concentration. The artificial neural network and response surface methodology were trained with Box-Behnken design data to predict responses, and to develop and compare each model's predictive abilities . The accuracy of results indicates that both models predict the experimental data for fatty acid methyl ester yields with high correlation coefficients (R 2 ) 0.94 and 0.92, respectively for artificial neural network and response surface methodology. The potential for producing biodiesel from C. pyrenoidosa is validated by the high yields of C18 fatty acid methyl esters. Experimental analysis demonstrated biodiesel quality in comparison with European and US standards.
研究领域:
生物工程
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可再生能源
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DOI
高影响因子
FOOD CHEMISTRY
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2020
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IF 6.306
Jiao Yang,Yun Huang,Haiyu Xu,Dongyu Gu,Fa Xu,Jintian Tang,Chen Fang,Yi Yang
The fermentation products of edible fungi are rich in anthraquinones and have a variety of activities, including the antioxidant activity. Because of the large number of combinations, it is very difficult to obtain the optimal multi-strains co-fermentation to improve the yield of anthraquinone. In the present study, an intelligent model based on artificial neural networks (ANNs) using backpropagation (BP) and radial basis function (RBF) algorithms was developed and validated to predict the anthraquinone contents in 136 two fungi and 680 three fungi co-fermented products. After experimental validation of the anthraquinone contents, the mean absolute error and the mean bias error of the results from RBF ANN were lower than those from BP ANN. The results indicated that the anthraquinone contents in A. bisporus , C. comatus and H. erinaceus co-fermentation product was the highest (2.11%). Furthermore, this co-fermentation product showed strong antioxidant activity.
研究领域:
生物技术
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食品科学
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DOI
高影响因子
International Journal of Food Engineering
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2020
·Vol. 16
·期 3
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IF 1.022
Yushuang Lu,Xianrui Liang,Lishuang Cheng,Sheng Fang
The aim of this study was to develop an environmentally friendly process to extract anthocyanins from blueberry pomace using water as a solvent and directly microencapsulate anthocyanins by spray drying. The anthocyanins in water and ethanol extracts were characterized by high-performance liquid chromatography and mass spectrometry. The malvidin-3-O-galactoside and malvidin-3-O-glucoside were identified as the main anthocyanins in the blueberry pomace. The anthocyanins profiles of water extracts were similar to that by ethanol extraction. The effects of extraction parameters including solid-to-liquid ratio and temperature on the extraction efficiency and anthocyanins concentration were studied. The blueberry anthocyanins degraded at temperatures higher than 60 °C and all anthocyanins showed similar degradation tendency. The result showed that the artificial neural network (ANN) modeling could be well used to portray the effects of these parameters. Finally, the water extracts were successfully spray dried to produce microencapsulation of blueberry anthocyanins with maltodextrin (MD) as wall materials.
研究领域:
食品科学
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植物化学
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DOI
高影响因子
MICROCHEMICAL JOURNAL
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2024
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IF 4.9
Jiawei Zhai,Bin Luo,Tianyang Liu,Hongtu Dong,Xiaotong Jin,Xiaodong Wang,Chunjiang Zhao
The rapid development of modern agricultural biotechnology and information technology has become a key driver of modern agricultural growth. The demand for ion detection is prevalent in all aspects of agriculture. This study details the research on an ion sensor agricultural system using potassium (K) ion-selective electrodes (ISEs). The main emphasis is on the application of artificial neural networks (ANNs) for driving ISE development of accuracy. ISEs based on solid contact with metal oxides, specifically manganese dioxide, are studied. Moreover, the design details of an ion signal acquisition device using the NI-9205 acquisition card and LabVIEW are presented. Finally, based on experimental results of nutrient solution samples, ANNs show better performance than the multiple linear regression method. The mean relative error of K-ISE detection was maintained within 4%. This study thoroughly discusses ISE technology to promote its adoption in agriculture.
研究领域:
农业生物技术
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信息科学
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DOI
高影响因子
JOURNAL OF APPLIED POLYMER SCIENCE
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2024
·Vol. 141
·期 26
·e55580
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IF 2.7
Zhenfeng Ma,Zhaoguang Zheng,Jun Zeng,Guodong Feng,Yihua Huang,Wenhui Luo,Minyou He,Jiaming Cui,Yi-Jun Liang
The practical application of fiber membranes prepared by electrostatic spinning requires a uniform thickness. However, the parameters involved in the spinning process are highly complex, particularly when it comes to precise control over membrane thickness. Hence, achieving a consistent and homogeneous thickness for fiber membranes has posed significant challenges thus far. To accurately predict membrane thickness during the spinning process and ensure consistent outcomes, this study employed polyvinylidene fluoride as the raw material and optimized electrostatic spinning preparation using an innovative airflow-assisted strategy. The as-proposed preparation strategy effectively enhances the volatilization of solvents within the jet, also promotes jet stretching through assistance from airflow, resulting in a more stable and homogeneous fiber diameter. Additionally, COMSOL simulations were utilized to provide rational explanations for these findings. By collecting multiple process parameters, including positive voltage, liquid supply speed, winding speed, and solution concentration, this study has successfully developed an artificial neural network (ANN) that exhibits reliable predictive capabilities for membrane thickness. Notably, the model demonstrates a remarkable goodness-of-fit with an R 2 value of 0.9981. Through the integration of ANN with electrostatic spinning technology, significant advancements have been achieved in accurately predicting and controlling the uniformity of electrostatically spun polymer fiber membranes.
研究领域:
材料科学
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生物医学工程
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产品关联
DOI
高影响因子
ACTA MATERIALIA
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2024
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IF 8.3
Xingqi Liao,Mingqing Liao,Chong Wei,Zhiheng Huang,Wenjiu Duan,Xiaoming Duan,Delong Cai,Laurent Gremillard,Zhihua Yang,Dechang Jia,Yu Zhou
For the engineering applications of freeze-cast porous ceramics, the demand targets are often multiple and competing, which is a challenging problem to seek a Nash equilibrium in the high-dimensional design space. An accurate and robust quantification of process-structure-property correlations would provide an effective path to find the set of Pareto optimal materials for one specific need. In this work, using porous Si 3 N 4 -Si 2 N 2 O ceramics as the model materials, a hybrid model for the quantitative design of the microstructure and mechanical properties is developed from four physics-based process-microstructure models with sintering, solidification, phase transformation and grain growth kinetic theories, and the subsequent data-driven structure-property model utilizing a machine learning method, artificial neural network (ANN). The SHapely Additive exPlanations (SHAP) analysis is further introduced to interpret the ANN model and mathematically identify the contribution of each microstructure feature descriptor toward target mechanical property outputs. These results present a systematic understanding of the process-structure-property relationships through the hybrid model, guiding the optimal design of the freeze-cast porous ceramics with required microstructures and mechanical properties.
研究领域:
材料科学
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机器学习
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工程学
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DOI
高影响因子
SEPARATION AND PURIFICATION TECHNOLOGY
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2025
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IF 9
Wang Tang,Tianxiong Liu,Hongxia Gao,Shaofei Wang,Min Zhou,Ningbo Yu,Zhiwu Liang
In this study, the viscosity of 12 alkanolamine or diamine aqueous solutions was measured at atmospheric pressure, with amine mass fractions ranging from 15 % to 100 % and temperatures ranging from 293.15 K to 353.15 K. An empirical model was used to correlate the viscosity experimental results of the 12 alkanolamine or diamine systems. Building upon the empirical model, the influence of chemical structure on viscosity was further explored, and two artificial neural networks with different data partitioning schemes, namely R-ANN and C-ANN, were developed. The mean absolute error (MAE) of the R-ANN and C-ANN models were 0.42 and 0.53, respectively. The evaluation results demonstrated that the R-ANN model effectively predicted the viscosity of the 12 alkanolamine and diamine systems. Additionally, the C-ANN model showed reliable predictive performance on a test set consisting of new amines—those not included in the training set. Finally, the structure–activity relationship between amine structure and viscosity was analyzed by calculating the electrostatic potential (ESP) of amine molecules. This research provides an effective theoretical framework and computational approach for predicting and understanding the viscosity of amine aqueous solutions.
研究领域:
化学工程
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计算化学
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产品关联
DOI
高影响因子
LWT-FOOD SCIENCE AND TECHNOLOGY
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2021
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IF 4.952
Shuaizhong Zhang,Yongkai Yuan,Chengzhen Liu,Yong Yang,Dandan Zhang,Shuang Liu,Dongfeng Wang,Ying Xu
The purpose of this study was to establish an efficient modern strategy for the reduction and control of cadmium (Cd) content in the crab viscera homogenate. To this end, the coupling of an artificial neural network (ANN) technique with the genetic algorithm (GA) method and compared the performance of GA-ANN with response surface methodology (RSM) were demonstrated in the prediction and optimization of the ultrasonic-assisted adsorption of Cd using a aerogel adsorbent. On the basis of single factor test, the data sample was established by Box–Behnken Design, and the effects of ultrasonic time, temperature, the addition amount of aerogel, and pH on the removal rate of Cd were researched. The results revealed that the RSM and GA-ANN models had relative errors and coefficients of determination R 2 values of 3.90%, 0.8440, and 1.79%, 0.9283, respectively, suggesting that GA-ANN is more accurate than RSM. The maximum percentage of Cd extracted (79.13%) was obtained using GA-ANN model as ultrasonic time of 87 min, temperature of 81 °C, adsorbent dosage of 0.07 g, pH of 3.20. In conclusion, the present results provide an effective method for optimizing multi-technology coupling to reduce the toxic heavy metal content in aquatic products.
研究领域:
环境科学
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食品科学
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DOI
高影响因子
Scientific Reports
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2016
·Vol. 6
·期 1
·1-11
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IF 5.228
Li Xuewu,Shi Tian,Liu Cong,Zhang Qiaoxin,Huang Xingjiu
Aluminum alloys are vulnerable to penetrating and peeling failures in seawater and preparing a barrier coating to isolate the substrate from corrosive medium is an effective anticorrosion method. Inspired by the lotus leaves effect, a wetting alloy surface with enhanced anticorrosion behavior has been prepared via etch, deposition, and low-surface-energy modification. Results indicate that excellent superamphiphobicity has been achieved after the modification of the constructed hierarchical labyrinth-like microstructures and dendritic nanostructures. The as-prepared surface is also found with good chemical stability and mechanical durability. Furthermore, superior anticorrosion behaviors of the resultant samples in seawater are investigated by electrochemical measurements. Due to trapped air in micro/nanostructures, the newly presented solid-air-liquid contacting interface can help to resist the seawater penetration by greatly reducing the interface interaction between corrosive ions and the superamphiphobic surface. Finally, an optimized two-layer perceptron artificial neural network is set up to model and predict the cause-and-effect relationship between preparation conditions and the anticorrosion parameters. This work provides a great potential to extend the applications of aluminum alloys especially in marine engineering fields.
研究领域:
材料科学
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防腐蚀工程
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表面工程
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产品关联
DOI
PubMed
高影响因子
RSC Advances
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2019
·Vol. 9
·期 26
·14797-14808
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IF 3.049
Jiashuai Wang,Zhe Li,Shaocun Yan,Xue Yu,Yanqing Ma,Lei Ma
An improved activated carbon material is synthesized from nostoc flagelliforme algae (NF) using an acid immersing method. The material has more pores and lower internal resistance compared with raw NF. Hydrofluoric acid can effectively decompose cellulose fibers and remove inorganic impurities, giving the carbon materials high mesopore volumes, which makes electrolyte ions rapidly transfer to the active site on the electrode surface. The specific capacitance of the sample was increased from 200 to 283 F g−1 after immersing in hydrofluoric acid. In addition, the symmetric supercapacitor shows an excellent energy density of 22 W h kg−1 at a power density of 80 W kg−1. The capacitance remains at 101.7% after 10 000 cycles. Furthermore, in order to find the relationship between the biochar structure and electrochemical performance in supercapacitors, an artificial neural network (ANN) method is used for studying the complex synergy mechanism. The specific capacitance is modelled using various input factors like aspect ratio (rL/D), cellulose ratio (CL(%)), specific surface area (SBET), pore volume (Vtot), internal resistance (Rs) and so on. The Levenberg–Marquart back propagation algorithm with sigmoid and ReLu active function is adopted to train the model. Random forest is used to analyse the relative importance of every input factor on specific capacitance. Results show that the model can estimate the energy storage with a mean squared error of 4.39 for materials with specific structure. Importance analyses indicate the first three significant variables are SBET, Rs and Vpor. The ANN model can accurately predict the electrical properties of biomass-based carbon materials, and also provide guidance for the selection of energy storage materials in the future.
研究领域:
材料科学
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能源存储
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生物工程
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产品关联
DOI
PubMed
高影响因子
SEPARATION AND PURIFICATION TECHNOLOGY
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2021
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IF 7.312
Yongjun Sun,Yuanyuan Yu,Shengbao Zhou,Kinjal J. Shah,Wenquan Sun,Jun Zhai,Huaili Zheng
In this study, an amphoteric magnetic chitosan (CS)-based flocculant MFe 3 O 4 @CS-g-PIA was prepared from CS, Fe 3 O 4 , and itaconic acid (IA), and its apparent morphology and characteristic structure were systematically studied. The flocculation performance and mechanism of the fabricated material were also investigated in different pollution systems, and the effects of total monomer concentration, m(CS):m(IA), IA pre-neutralization degree, reaction temperature, reaction time, and initiator concentration on the synthesis of MFe 3 O 4 @CS-g-PIA were studied. Characterization results showed that MFe 3 O 4 @CS-g-PIA forms a three-dimensional network with excellent magnetic induction. The optimal removal rates of Cu(II) and Disperse Blue 56 (DB56; 90.2% and 97.0%, respectively) were obtained under the conditions of 150 mg·L −1 MFe 3 O 4 @CS-g-PIA, pH 6.0, and 300 rpm stirring speed. MFe 3 O 4 @CS-g-PIA maintained removal rates of over 80.0% for Cu(II) and DB56 after five consecutive cycles of regeneration/flocculation and demonstrated excellent acid resistance stability. Changes in the particle size distribution, fractal dimensions, and zeta potentials of the flocs indicated that the relevant flocculation mechanism involves the synergistic functions of chelation, charge neutralization, and adsorption bridging. An artificial neural network model was finally established on the basis of the experimental flocculation data to predict the removal rates of Cu(II) (R = 0.97) and DB56 (R = 0.98) accurately.
研究领域:
环境科学
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材料科学
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化学工程
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产品关联
DOI
高影响因子
Frontiers of Environmental Science & Engineering
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2022
·Vol. 17
·期 1
·1-12
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IF 6.048
Yin Xiaoxiao,Tao Junyu,Chen Guanyi,Yao Xilei,Luan Pengpeng,Cheng Zhanjun,Li Ning,Zhou Zhongyue,Yan Beibei
Pyrolysis is considered an attractive option and a promising way to dispose waste plastics. The thermogravimetric experiments of high-density polyethylene (HDPE) were conducted from 105 °C to 900 °C at different heating rates (10 °C/min, 20 °C/min, and 30 °C/min) to investigate their thermal pyrolysis behavior. We investigated four methods including three model-free methods and one model-fitting method to estimate dynamic parameters. Additionally, an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss (wt.%) of HDPE, and optimized via assessing mean squared error and determination coefficient on the test set. The optimal MSE (2.6297 × 10 −2 ) and R 2 value ( R 2 > 0.999) were obtained. Activation energy and pre-exponential factor obtained from four different models achieves the acceptable value between experimental and predicted results. The relative error of the model increased from 2.4 % to 6.8 % when the sampling frequency changed from 50 s to 60 s, but showed no significant difference when the sampling frequency was below 50 s. This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space. This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters. It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics.
研究领域:
化学工程
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材料科学
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环境科学
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DOI
高影响因子
危险品化学品经营许可证（带存储）