INFLUENCED FACTORS IN THE DELIGNIFICATION PROCESS OF RED MERANTI WOOD SAWDUST

Lignocellulosic biomass are plants that include forestry residue and agricultural residues that are mainly composed of cellulose, hemicelluloses and lignin. Red Meranti wood sawdust (RMWS) are one of lignocellulosic biomass that rich-cellulose content. To obtain cellulose, the pretreatments are needed to extract it from outer layer of lignin and hemicellulose by using the acid-chlorite delignification procedure aided with design of experimental from Design Expert 7.1 software. Four factors were selected in design of experiment using two level with half fraction factorial analysis were came out with total of 8 runs. The factors contributed were ratio acetic acid (AC) to RMWS (0.45 and 0.6), ratio sodium chlorite to RMWS (0.6 and 1.64), reaction time (4hr and 6hr) and temperature (55°C and 75°C). The results obtained were showed that the design model was substantial resulting with a coefficient of determination value of 0.9963. Two factors that generated the highest to the process were ratio SC to RMWS (B) and temperature (D). The percentage error between the actual and predicted value for lignin removal at 0.79% and 4.92%, which found to be less than 5%, and thus, the model was successfully validated.

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A Review of Different Types of DOE Methods as a Useful Platform for Improving the Performance of Nano Adsorbents in Removal Systems of Pollutants

Nanoscience &Nanotechnology-Asia ◽

10.2174/2210681209666190220130002 ◽

2020 ◽

Vol 10 (3) ◽

pp. 219-227

Author(s):

Ali Behmaneshfar ◽

Abdolhossein Sadrnia ◽

Hassan Karimi-Maleh

Keyword(s):

Response Surface Methodology ◽

Design Of Experiments ◽

Web Of Science ◽

Computer Software ◽

Coefficient Of Determination ◽

Design Expert ◽

Number Of Factors ◽

Different Types ◽

Matlab Programming ◽

Nano Adsorbent

Background: In recent years, the Design of Experiments (DOE) is used for removing pollutant from wastewater by nano-adsorbent. Some methods are Taguchi, Response Surface Methodology (RSM) and factorial design. The aim of this paper is to review different used methods of DOE in removing pollutant to suggest some notations to scholars. Methods: The reviewed papers were searched in Google Scholar, Scopus, and Web of Science randomly and categorized based on DOE methods. Results: Number of factors and responses in DOE for removing pollutants from wastewater are between 2-6 and 1-4, respectively. There are several computer software programs that provide simple use of these methods, such as Qualitek, Design Expert, Minitab, R and Matlab Programming. All models have a coefficient of determination R-sq more than 0.9. Conclusion: All the mentioned methods are appropriate because of the high R-sq value. Since the largest number of runs are used in RSM, it is not suitable for the experiments which are conducted by expensive materials and process. Furthermore, Design Expert and Minitab are the most popular software used by scholars in DOE methods for the removal of pollutant.

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Mathematical Problems in Engineering ◽

10.1155/2020/8642430 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Shengpu Li ◽

Yize Sun

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Least Squares ◽

Transfer Rate ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Experimental Sample ◽

Machine Model ◽

Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

Epidemiology and Infection ◽

10.1017/s0950268821000091 ◽

2021 ◽

Vol 149 ◽

Author(s):

Junwen Tao ◽

Yue Ma ◽

Xuefei Zhuang ◽

Qiang Lv ◽

Yaqiong Liu ◽

...

Keyword(s):

Variable Selection ◽

Mean Squared Error ◽

Moving Average ◽

Dynamic Bayesian Networks ◽

Environmental Data ◽

Control Measures ◽

Coefficient Of Determination ◽

Percentage Error ◽

Analysis Strategy ◽

Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

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Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽

10.3390/s21144655 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4655

Author(s):

Dariusz Czerwinski ◽

Jakub Gęca ◽

Krzysztof Kolano

Keyword(s):

Machine Learning ◽

Temperature Measurement ◽

Stochastic Gradient Descent ◽

Estimation Accuracy ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Bldc Motor ◽

Temperature Estimation ◽

Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

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Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽

10.3390/pr9071166 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1166

Author(s):

Bashir Musa ◽

Nasser Yimen ◽

Sani Isah Abba ◽

Humphrey Hugh Adun ◽

Mustafa Dagbasi

Keyword(s):

Support Vector Regression ◽

Mean Square Error ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Load Forecasting ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

Journal of Solar Energy ◽

10.1155/2016/8197389 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

Boluwaji M. Olomiyesan ◽

Onyedi D. Oyedum

Keyword(s):

Solar Radiation ◽

Comparative Study ◽

Global Radiation ◽

Statistical Error ◽

Global Solar Radiation ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Percentage Error ◽

Radiation Data ◽

Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

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Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Journal of Clinical Medicine ◽

10.3390/jcm9030674 ◽

2020 ◽

Vol 9 (3) ◽

pp. 674 ◽

Cited By ~ 83

Author(s):

Mohammed A. A. Al-qaness ◽

Ahmed A. Ewees ◽

Hong Fan ◽

Mohamed Abd El Aziz

Keyword(s):

Relative Error ◽

Computing Time ◽

World Health ◽

Coefficient Of Determination ◽

Flower Pollination Algorithm ◽

Percentage Error ◽

Anfis Model ◽

Local Optima ◽

Inference System ◽

Proposed Model

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

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A novel high performance metagenome-derived alkali-thermostable endo-β-1,4-glucanase for lignocellulosic biomass hydrolysis in the harsh conditions

10.21203/rs.2.23802/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Shohreh Ariaeenejad ◽

Atefeh Sheykhabdolahzadeh ◽

Morteza Maleki ◽

Kaveh Kavousi ◽

Mehdi Foroozandeh Shahraki ◽

...

Keyword(s):

Enzymatic Hydrolysis ◽

Lignocellulosic Biomass ◽

Rice Straw ◽

Specific Activity ◽

Agricultural Waste ◽

Extreme Temperature ◽

In Silico Analysis ◽

Cellulose Content ◽

Biomass Hydrolysis ◽

Harsh Condition

Abstract Background: Lignocellulosic biomass, is a great resource for the production of bio-energy and bio-based material since it is largely abundant, inexpensive and renewable. The requirement of new energy sources has led to a wide search for novel effective enzymes to improve the exploitation of lignocellulose, among which the importance of thermostable and halotolerant cellulase enzymes with high pH performance is significant. Results: The primary aim of this study was to discover a novel alkali-thermostable endo-β-1,4-glucanase from the sheep rumen metagenome. Using a multi-step in-silico analysis, primary candidates with desired properties were found and subjected to cloning, expression, and purification followed by functional and structural characterization. The enzymes' kinetic parameters, including V max , Km, and specific activity, were calculated. The PersiCel4 demonstrated its optimum activity at pH 8.5 and a temperature of 85°C and was able to retain more than 70% of its activity after 150 hours of storage at 85°C. Furthermore, this enzyme was able to maintain its catalytic activity in the presence of different concentrations of NaCl, MgCl 2 , CaCl 2 , and MnCl 2 . Our results showed that treatment with MnCl 2 could enhance the enzyme’s activity by 89%. PersiCel4 was ultimately used for enzymatic hydrolysis of autoclave pretreated rice straw, the most abundant agricultural waste with rich cellulose content. In autoclave treated rice straw, enzymatic hydrolysis with the PersiCel4 increased the release of reducing sugar up to 260% after 72 hours in the harsh condition ( T= 85°C, pH = 8.5). Conclusion: Considering the urgent demand for stable cellulases that are operational on extreme temperature and pH conditions and due to several proposed distinctive characteristics of PersiCel4, it can be used in the harsh condition for bioconversion of lignocellulosic biomass.

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KAJIAN MACAM LIMBAH DAN PENAMBAHAN TEPUNG TONGKOL JAGUNG TERHADAP PERTUMBUHAN DAN HASIL JAMUR TIRAM (Pleurotus ostreatus)

Agric ◽

10.24246/agric.2016.v28.i1.p1-6 ◽

2016 ◽

Vol 28 (1) ◽

pp. 1

Author(s):

Agung Setyarini ◽

Nugraheni Retnaningsih

Keyword(s):

Body Weight ◽

Effective Interaction ◽

Fruit Body ◽

Raw Material ◽

Growth And Yield ◽

Oyster Mushroom ◽

Fruiting Bodies ◽

Corn Cob ◽

Wood Sawdust ◽

Two Factors

<span class="fontstyle0">One limiting for factor the production of oyster mushrooms was difficult to obtain the raw material of sengon wood sawdust, meanwhile, the production of oyster mushroom necessary need the nutrients in the form of bran or cornmeal. The purpose of this study was to study planting medium, the concentration of corn cob flour, and to find the effective interaction of these two factors on growth and yield of oyster mushroom. This study used a completely randomized design (CRD) with two factors, concentration of media and corn cob flour. The media used in this study are sengon sawdust, glugu sawdust, acacia wood sawdust, rice straw and bagasse, while the concentration of corn cob flour was 0% per baglog, 1% per baglog, 2% per baglog, 3% per baglog and 4% per baglog. Data analysis was using F test level 5% and continued with Duncan test. The results of this study showed that sawdvst sengon media generally give better effect to the growth and yield of oyster mushroom, while corn cob flour treatment concentration was not known exactly in enhancing the growth and yield of oyster mushroom. Treatment of media accelerate the deployment of mycelium old, when appearing pin head, increasing the number of fruiting bodies in a single clump and increasing the mushroom fruit body weight. Extra flour treatment corncob accelerate as emerging pin head, increasing the number of fruiting bodies in a clump and increase total body weight of mushrooms.</span>

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Determination of Low-Level Agricultural Residues in Soft Drinks and Sports Drinks by Liquid Chromatography/Tandem Mass Spectrometry: Single-Laboratory Validation

Journal of AOAC International ◽

10.1093/jaoac/90.2.521 ◽

2007 ◽

Vol 90 (2) ◽

pp. 521-533 ◽

Cited By ~ 8

Author(s):

Nathan Paske ◽

Bryan Berry ◽

John Schmitz ◽

Darryl Sullivan

Keyword(s):

Mass Spectrometry ◽

Liquid Chromatography ◽

Tandem Mass Spectrometry ◽

Pesticide Residues ◽

Agricultural Residues ◽

Soft Drinks ◽

Coefficient Of Determination ◽

Tandem Mass ◽

Sports Drinks ◽

Relative Standard

Abstract In this study, sponsored by PepsiCo Inc., a method was validated for measurement of 11 pesticide residues in soft drinks and sports drinks. The pesticide residues determined in this validation were alachlor, atrazine, butachlor, isoproturon, malaoxon, monocrotophos, paraoxon-methyl, phorate, phorate sulfone, phorate sulfoxide, and 2,4-dichlorophenoxyacetic acid (2,4-D) when spiked at 0.100 g/L (1.00 g/L for phorate). Samples were filtered (if particulate matter was present), degassed (if carbonated), and analyzed using liquid chromatography with tandem mass spectrometry. Quantitation was performed with matrix-matched external standard calibration solutions. The standard curve range for this assay was 0.0750 to 10.0 g/L. The calibration curves for all agricultural residues had coefficient of determination (r2) values greater than or equal to 0.9900 with the exception of 2 values that were 0.9285 and 0.8514. Fortification spikes at 0.100 g/L (1.00 g/L for phorate) over the course of 2 days (n = 8 each day) for 3 matrixes (7UP, Gatorade, and Diet Pepsi) yielded average percent recoveries (and percent relative standard deviations) as follows (n = 48): 94.4 (15.2) for alachlor, 98.2 (13.5) for atrazine, 83.1 (41.6) for butachlor, 89.6 (24.5) for isoproturon, 87.9 (24.4) for malaoxon, 96.1 (9.26) for monocrotophos, 101 (25.7) for paraoxon-methyl, 86.6 (20.4) for phorate, 101 (16.5) for phorate sulfone, 93.6 (25.5) for phorate sulfoxide, and 98.2 (6.02) for 2,4-D.

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