scholarly journals Chlorella vulgaris logistic growth kinetics model in high concentrations of aqueous ammonia

2018 ◽  
Vol 19 (2) ◽  
pp. 1-9
Author(s):  
Azlin Suhaida Azmi ◽  
NURAIN ATIKAH CHE AZIZ ◽  
Noor Illi Mohamad Puad ◽  
Amanatuzzakiah Abdul Halim ◽  
Faridah Yusof ◽  
...  
Keyword(s):  
Chlorella Vulgaris ◽  
Aqueous Ammonia ◽  
Maximum Growth ◽  
Coefficient Of Determination ◽  
Logistic Growth ◽  
Fluorescent Light ◽  
Growth Data ◽  
Model Coefficient ◽  
Logistic Growth Model ◽  
High Concentrations

ABSTRACT: The ability of microalgae to utilize CO2 during photosynthesis and grow rapidly shows their potential in CO2 bio-fixation to capture and store the gas. However, CO2 capture by this biological approach is very slow compared to chemical reaction-based processes such as absorption using amine or aqueous ammonia. Integration between chemical (aqueous ammonia) and biological (microalgae) aspects might enhance the capturing process and at the same time the microalgae can assimilate CO2 for beneficial bioproduct formation. Thus, it is important to assess the growth of the microalgae in various concentrations of ammonia with CO2 supply. Hence, the main objective of this study is to investigate Chlorella vulgaris growth and its kinetics in aqueous ammonia. To achieve that, C. vulgaris was cultivated in various concentrations of aqueous ammonia between 0 to 1920 mg/L at room temperature (i.e. 27 °C) and supplied with 15% (v/v) of CO2 under illumination of 3500 lux of white fluorescent light. Result shows that the maximum growth capacity (Xmax) of C. vulgaris is deteriorating from 1.820 Au to 0.245 Au as the concentration of aqueous ammonia increased. However, no significant change in maximum specific growth rate (µmax) was observed. The growth data was then fitted into the logistic growth model. The model coefficient of determination (R2) is decreasing, which suggests modification of the model is required. ABSTRAK: Keupayaan alga-mikro untuk menggunakan CO2 semasa proses fotosintesis dan pembiakannya yang pesat menunjukkan potensi dalam penggunaan dan penyimpanan gas ketetapan-biologi. Walau bagaimanapun, penggunaan CO2  melalui cara ini adalah sangat perlahan berbanding proses tindak balas kimia melalui penyerapan amina ataupun cecair  ammonia. Percampuran antara tindak balas kimia (cecair ammonia) dan tindak balas biologi, memungkinkan penambahan proses percampuran dan pada masa sama alga-mikro akan menyerap CO2 bagi kepentingan pembentukan hasil biologi. Dengan itu, adalah sangat penting untuk mengawasi pertumbuhan alga-mikro dalam pelbagai ketumpatan ammonia bersama kandungan CO2. Oleh itu, objektif utama penyelidikan ini adalah untuk menyiasat pertumbuhan Chlorella vulgaris dan proses kinetik dalam cecair ammonia. Bagi memperoleh hasil tersebut,  C. vulgaris telah dikulturkan pada ketumpatan cecair berbeza antara 0 ke 1920 mg/L pada suhu bilik (iaitu 27 °C) dan dibekalkan dengan 15% (v/v) CO2 di bawah cahaya putih flurosen  3500 lux. Keputusan menunjukkan kapasiti pertumbuhan terbanyak (Xmax) C. vulgaris telah berkurang daripada 1.820 Au kepada 0.245 Au apabila ketumpatan cecair ammonia dikurangkan. Walau bagaimanapun, tiada perubahan ketara pada kadar pertumbuhan (µmax) dapat dilihat. Data kadar pertumbuhan kemudiannya dikemas kini pada model pertumbuhan logistik. Model pekali penentu (R2) telah direndahkan di mana cadangan untuk mengubah model adalah diperlukan.

Dynamic Predictive Model for Growth of Bacillus cereus from Spores in Cooked Beans

2017 ◽  
Vol 81 (2) ◽  
pp. 308-315 ◽  
Author(s):  
Vijay K. Juneja ◽  
Abhinav Mishra ◽  
Abani K. Pradhan
Keyword(s):  
Bacillus Cereus ◽  
Growth Rates ◽  
Linear Models ◽  
Growth Models ◽  
Maximum Growth ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Growth Data ◽  
Three Phase ◽  
Primary Model

ABSTRACT Kinetic growth data for Bacillus cereus grown from spores were collected in cooked beans under several isothermal conditions (10 to 49°C). Samples were inoculated with approximately 2 log CFU/g heat-shocked (80°C for 10 min) spores and stored at isothermal temperatures. B. cereus populations were determined at appropriate intervals by plating on mannitol–egg yolk–polymyxin agar and incubating at 30°C for 24 h. Data were fitted into Baranyi, Huang, modified Gompertz, and three-phase linear primary growth models. All four models were fitted to the experimental growth data collected at 13 to 46°C. Performances of these models were evaluated based on accuracy and bias factors, the coefficient of determination (R2), and the root mean square error. Based on these criteria, the Baranyi model best described the growth data, followed by the Huang, modified Gompertz, and three-phase linear models. The maximum growth rates of each primary model were fitted as a function of temperature using the modified Ratkowsky model. The high R2 values (0.95 to 0.98) indicate that the modified Ratkowsky model can be used to describe the effect of temperature on the growth rates for all four primary models. The acceptable prediction zone (APZ) approach also was used for validation of the model with observed data collected during single and two-step dynamic cooling temperature protocols. When the predictions using the Baranyi model were compared with the observed data using the APZ analysis, all 24 observations for the exponential single rate cooling were within the APZ, which was set between −0.5 and 1 log CFU/g; 26 of 28 predictions for the two-step cooling profiles also were within the APZ limits. The developed dynamic model can be used to predict potential B. cereus growth from spores in beans under various temperature conditions or during extended chilling of cooked beans.

scholarly journals Estimating the critical points of an epidemic: An application of non-epidemiological logistic growth model for COVID-19

2020 ◽  
Author(s):  
Ivan Bezerra Allaman ◽  
Enio Galinkin Jelihovschi
Keyword(s):  
Growth Model ◽  
Critical Points ◽  
Maximum Growth ◽  
Logistic Growth ◽  
Variable Response ◽  
Variable Time ◽  
Epidemic Curve ◽  
Acceleration And Deceleration ◽  
Logistic Growth Model ◽  
Asymptotic Point

Abstract Epidemiological models have become a very important tool in understanding an epidemic development, mainly because they help researchers in finding good and new strategies in their fight against its spread. Several models have been proposed up to now, some are mathematical others apply models from other areas. The SIR and SEIR among others, mainly focus on the variable response and on epidemiological parameters as the basic reproduction number (R0) and infection rate per unit of time, nevertheless they do not focus on the variable ‘time’. We propose the use of the variable time using the logistic model as it is generally used to describe the growth of animals. This model is important because it allows the estimation of the points of acceleration and deceleration, the point of maximum growth and the asymptotic point of the epidemic. This is only possible when the epidemic curve is stable and has an ‘S’ shape. In this work we use the variable ‘accumulated cases’ of China and Italy and point out the main socioeconomic facts that occurred in each period of the estimated critical points from the logistic growth model.

scholarly journals Comparative Kinetic Study of Anaerobic Treatment of Thermally Pretreated Source-Sorted Organic Market Refuse

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Aritra Das ◽  
Chanchal Mondal
Keyword(s):  
Biogas Production ◽  
Anaerobic Treatment ◽  
Exponential Model ◽  
Coefficient Of Determination ◽  
Logistic Growth ◽  
Thermal Pretreatment ◽  
Production Potential ◽  
Potential Market ◽  
Different Temperatures ◽  
Logistic Growth Model

The present study deals with extensive investigations of the effect of thermal pretreatment on whole-sale market rejects for their biogas production potential. Market reject considered as biomass for this study has been treated at two different temperatures 85°C and 135°C for 8 h each and subjected as feedstock for anaerobic digestion (AD) process. The AD process has been operated in the mesophilic range (35–38°C) of bacterial growth. Various kinetic models have been used to simulate the experimental data. Kinetic modeling revealed that biogas production rate exhibited better coefficient of determination (R2) in the range of 0.973–0.989 with exponential model for the ascending limb whereas the descending limb resulted in good linear correlation withR2as 0.911–0.976. Logistic growth model and Gompertz relation simulation of cumulative biogas production resulted in betterR2values in the range of 0.994–0.997 and 0.998–0.999, whereas theR2values for exponential rise to maximum plots ranged from 0.722 to 0.800.

scholarly journals Predicting of the Coronavirus Disease 2019 (COVID-19) Epidemic Using Estimation of Parameters in the Logistic Growth Model

2021 ◽  
Vol 13 (2) ◽  
pp. 465-485
Author(s):  
Agus Kartono ◽  
Setyanto Tri Wahyudi ◽  
Ardian Arif Setiawan ◽  
Irmansyah Sofian
Keyword(s):  
Growth Model ◽  
Least Squares Method ◽  
Time Dependent ◽  
Actual Data ◽  
Coefficient Of Determination ◽  
Logistic Growth ◽  
Model Parameters ◽  
Time Interval ◽  
Non Linear ◽  
Logistic Growth Model

The COVID-19 pandemic was impacting the health and economy around the world. All countries have taken measures to control the spread of the epidemic. Because it is not known when the epidemic will end in several countries, then the prediction of the COVID-19 pandemic is a very important challenge. This study has predicted the temporal evolution of the COVID-19 pandemic in several countries using the logistic growth model. This model has analyzed several countries to describe the epidemic situation of these countries. The time interval of the actual data used as a comparison with the prediction results of this model was starting in the firstly confirmed COVID-19 cases to December 2020. This study examined an approach to the complexity spread of the COVID-19 pandemic using the logistic growth model formed from an ordinary differential equation. This model described the time-dependent population growth rate characterized by the three parameters of the analytical solution. The non-linear least-squares method was used to estimate the three parameters. These parameters described the rate growth constant of infected cases and the total number of confirmed cases in the final phase of the epidemic. This model is applied to the spread of the COVID-19 pandemic in several countries. The prediction results show the spread dynamics of COVID-19 infected cases which are characterized by time-dependent dynamics. In this study, the proposed model provides estimates for the model parameters that are good for predicting the COVID-19 pandemic because they correspond to actual data for all analyzed countries. It is based on the coefficient of determination, R2, and the R2 value of more than 95% which is obtained from the non-linear curves for all analyzed countries. It shows that this model has the potential to contribute to better public health policy-making in the prevention of the COVID-19 pandemic.

scholarly journals Estimation of the critical points of an epidemic by means of a logistic growth model

2021 ◽  
Author(s):  
Ivan Bezerra Allaman ◽  
Enio Galinkin Jelihovschi
Keyword(s):  
Growth Model ◽  
Critical Points ◽  
Maximum Growth ◽  
Logistic Growth ◽  
Variable Response ◽  
Variable Time ◽  
Epidemic Curve ◽  
Acceleration And Deceleration ◽  
Logistic Growth Model ◽  
Asymptotic Point

Abstract Epidemiological models have become a very important tool in understanding an epidemic’s development, mainly because they help researchers find more efficient strategies in their fight against its spread. Several models have been proposed up to now: some use fractional calculus to solve differential equations while others use applications from other areas such as predatorprey models. The SIR and SEIR models, among others, mainly focus on the variable response and on epidemiological parameters such as the basic reproduction number (R0) and infection rate per unit of time, nevertheless they do not focus on the variable ‘time’. We propose the use of the variable time, as the main variable, by using a reparametrization in the logistic model since it will lead to the understanding of the epidemic as it goes along the time. Moreover, this model is important because it allows the estimation of the points of acceleration and deceleration, the point of maximum growth and the asymptotic point of the epidemic. This is only possible by getting an stable epidemic curve with an ‘S’ shape. In this work we use the variable ‘accumulated cases’ of COVID-19 of China and Italy and point out the main socioeconomic facts that occurred in each period of the estimated critical points from the logistic growth model.

scholarly journals Extended logistic growth model for heterogeneous populations

2017 ◽  
Author(s):  
Wang Jin ◽  
Scott W McCue ◽  
Matthew J Simpson
Keyword(s):  
Cell Proliferation ◽  
Growth Model ◽  
Numerical Solutions ◽  
Cellular Level ◽  
Logistic Growth ◽  
Logistic Growth Model ◽  
Living Tissues ◽  
Discrete Mathematical Model ◽  
The Continuum

AbstractCell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity. Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.

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