scholarly journals The Influence of Change Load Against Fuel Cost in Coal-fired Power Tarahan Lampung Unit 3 and 4

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Endah Komalasari ◽  
Rahmattulloh Rahmattulloh ◽  
Herri Gusmedi
Keyword(s):  
Power Plant ◽  
Polynomial Regression ◽  
Electrical Power ◽  
Electric Utility ◽  
Second Order ◽  
Fuel Cost ◽  
Input Output ◽  
Side Load ◽  
Order Polynomial ◽  
Output Characteristics

Abstract—The operation of a power plant depends on fuel where fuel costs are incurred on a generating unit is a function of the plant load. The ability power plant carry the load determine the reliability of an electrical system, hence the power raised always be done equal to needs in side load all the time. Fluctuations demand of electrical power in side load will cause fluctuations change fuel cost. In this paper, the correlation both commonly called input-output characteristics of the power plant needs to be determined. These characteristics declared total input tons of coal per hour is used and net electrical output where the output power is available to the electric utility. One way to achieve this goal is to match polynomial regression on the data using a second order polynomial. This model allows the study of the  incremental fuel cost resulting from a change load in a coal-fired power plant.Keywords—Coal-fired power, economic dispatch, incremental cost characteristics, input-output characteristics, second order polynomial regression.

scholarly journals Combined Surfactant and Enzyme-Assisted Extraction of Pectin from Hawthorn (Crataegus monogyna Jacq.) Fruits

2022 ◽  
Vol 51 (4) ◽  
pp. 733-742
Author(s):  
Anastasia Novikova ◽  
Liubov Skrypnik
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Polynomial Regression ◽  
Polynomial Equation ◽  
Second Order ◽  
Wild Fruits ◽  
Assisted Extraction ◽  
Order Polynomial ◽  
Extraction Parameters ◽  
Enzyme Assisted Extraction

Introduction. Commercial pectin is usually obtained from apples or citrus fruits. However, some wild fruits, such as hawthorn, are also rich in pectin with valuable nutritional and medical properties. The research objective was to study and improve the process of combined surfactant and enzyme-assisted extraction of pectin from hawthorn fruits. Study objects and methods. The study involved a 1% solution of Polysorbate-20 surfactant and a mix of two enzymes, namely cellulase and xylanase, in a ratio of 4:1. The response surface methodology with the Box-Behnken experimental design improved the extraction parameters. The experiment featured three independent variables – temperature, time, and solvent-to-material ratio. They varied at three levels: 20, 40, and 60°C; 120, 180, and 240 min; 15, 30, and 45 mL per g. Their effect on the parameters on the pectin yield was assessed using a quadratic mathematical model based on a second order polynomial equation. Results and discussion. The response surface methodology made it possible to derive a second order polynomial regression equation that illustrated the effect of extraction parameters on the yield of polyphenols. The regression coefficient (R2 = 98.14%) and the lack-of-fit test (P > 0.05) showed a good accuracy of the model. The optimal extraction conditions were found as follows: temperature = 41°C, time = 160 min, solvent-to-material ratio = 32 mL per 1 g. Under the optimal conditions, the predicted pectin yield was 14.9%, while the experimental yield was 15.2 ± 0.4%. The content of galacturonic acid in the obtained pectin was 58.5%, while the degree of esterification was 51.5%. The hawthorn pectin demonstrated a good complex-building ability in relation to ions of copper (564 mg Cu2+/g), lead (254 mg Pb2+/g), and cobalt (120 mg Co2+/g). Conclusion. Combined surfactant and enzyme-assisted extraction made improved the extraction of pectin from hawthorn fruits. The hawthorn pectin can be used to develop new functional products.

scholarly journals Model Lagrange Multiplier Pada Pembangkit Listrik Tenaga Gas Sektor Keramasan Untuk Pemakaian Bahan Bakar

2020 ◽  
Vol 4 (2) ◽  
pp. 381
Author(s):  
Dedy Hermanto ◽  
Feby Ardianto
Keyword(s):  
Power Plant ◽  
Lagrange Multiplier ◽  
Lagrange Multiplier Method ◽  
Multiplier Method ◽  
Economic Optimization ◽  
Input Output ◽  
Plant System ◽  
Output Characteristics ◽  
Fuel Costs ◽  
Method Approach

Operasi ekonomis  sistem pembangkit tenaga listrik pada pembangkitan keramasan yang terdiri dari unit PLTG, PLTGU 1 dan PLTGU2  dilakukan dengan  jalur pembagian penjadwalan  atau economic dispath dari masing-masing pembangkit sehingga operasi secara optimum sistem pembangkit tenaga listrik dapat mencapai biaya bahan bakar yang minimum.  Tujuan penelitian optimasi ekonomis pada operasi sistem pembangkit tenaga listrik dengan metode langrange multiplier. Metode penelitian dilakukan menggunakan  pendekatan  metode Lagrange Multiplier dengan 4 tahapan, yaitu: 1. Perhitungan Karakteristik Input-Output Unit-unit Pembangkit; 2. Pemodelan Matematis Koefisien Persamaan Karakteristik Input-Output; 3. Perhitungan Dengan Pendekatan Metoda Lagrange Multiplier; 4. Analisis . Hasil perhitungan, biaya  terbesar  yang  dipergunakan  pada  perusahaan  listrik  adalah  biaya  bahan  bakar,  sehingga  dalam  perencanaan  operasi  sistem agar  biaya  bahan  bakar  serendah  mungkin, dicapai  biaya  bahan  bakar  yang  optimum, dengan  tetap  memperhatikan kendala-kendala sistem seperti kemampuan pembangkit dari generator. Beban sistem sebesar 50,98 MW sampai dengan 63,91 MW, dengan mengoperasikan PLTGU 1, PLTGU 2 dan PLTG Keramasan menghasilkan penghematan biaya bahan bakar  Rp.443.4600,54 sampai dengan Rp. 616.598,83per-jam.The economical operation of the power plant system in the generation of keramasan which consists of PLTG, PLTGU 1 and PLTGU2 units is carried out by sharing scheduling or economic dispath from each plant so that the optimum operation of the power generation system can achieve minimum fuel costs. The research objective of economic optimization in the operation of a power plant system using the Langrange multiplier method. The research method was carried out using the Lagrange Multiplier method approach with 4 stages, namely: 1. Calculation of Input-Output Characteristics of Generating Units; 2. Mathematical Modeling of the Input-Output Characteristics Coefficient; 3. Calculation with Lagrange Multiplier Method Approach; 4. Analysis. The results of the calculation, the largest cost used by the power company is the cost of fuel, so that in planning the system operation to keep fuel costs as low as possible, optimum fuel costs are achieved, while still paying attention to system constraints such as the power of the generator. The system load is 50.98 MW to 63.91 MW, by operating PLTGU 1, PLTGU 2 and PLTG Keramasan resulting in fuel cost savings of Rp. 443.4600.54 to Rp. 616,598.83 per hour.

scholarly journals Methane Emission Characteristics of Naturally Ventilated Cattle Buildings

2020 ◽  
Vol 12 (10) ◽  
pp. 4314
Author(s):  
Sabrina Hempel ◽  
Diliara Willink ◽  
David Janke ◽  
Christian Ammon ◽  
Barbara Amon ◽  
...  
Keyword(s):  
Methane Emission ◽  
Polynomial Regression ◽  
Methane Emissions ◽  
Second Order ◽  
Greenhouse Gas Mitigation ◽  
Empirical Modeling ◽  
Pollutant Emissions ◽  
Seasonal Temperature ◽  
Concrete Floor ◽  
Order Polynomial

The mandate to limit global temperature rise calls for a reliable quantification of gaseous pollutant emissions as a basis for effective mitigation. Methane emissions from ruminant fermentation are of particular relevance in the context of greenhouse gas mitigation. The emission dynamics are so far insufficiently understood. We analyzed hourly methane emission data collected during contrasting seasons from two naturally ventilated dairy cattle buildings with concrete floor and performed a second order polynomial regression. We found a parabolic temperature dependence of the methane emissions irrespective of the measurement site and setup. The position of the parabola vertex varied when considering different hours of the day. The circadian rhythm of methane emissions was represented by the pattern of the fitted values of the constant term of the polynomial and could be well explained by feeding management and air flow conditions. We found barn specific emission minima at ambient temperatures around 10 °C to 15 °C. As this identified temperature optimum coincides with the welfare temperature of dairy cows, we concluded that temperature regulation of dairy cow buildings with concrete floor should be considered and further investigated as an emission mitigation measure. Our results further indicated that empirical modeling of methane emissions from the considered type of buildings with a second order polynomial for the independent variable air temperature can increase the accuracy of predicted long-term emission values for regions with pronounced seasonal temperature fluctuations.

An Exact Algorithm for Hierarchically Well-Formulated Subsets in Second-Order Polynomial Regression

Technometrics ◽  
2009 ◽  
Vol 51 (3) ◽  
pp. 306-315 ◽  
Author(s):  
Michael J. Brusco ◽  
Douglas Steinley ◽  
J. Dennis Cradit
Keyword(s):  
Polynomial Regression ◽  
Exact Algorithm ◽  
Second Order ◽  
Order Polynomial

scholarly journals An empirical study of Taiwan’s food security index

2009 ◽  
Vol 13 (7) ◽  
pp. 1056-1063 ◽  
Author(s):  
Chih-Yang Yeh ◽  
Pei-San Liao ◽  
Chieh-Yu Liu ◽  
Jeng-Fu Liu ◽  
Hsing-Yi Chang
Keyword(s):  
Regression Model ◽  
Polynomial Regression ◽  
Energy Requirement ◽  
Second Order ◽  
Activity Level ◽  
Dietary Energy ◽  
Order Polynomial ◽  
The Status ◽  
Level Of Economic Development ◽  
Polynomial Regression Model

AbstractObjectiveThe FAO has developed an approach for estimating the prevalence of undernourishment. Based on the FAO method Taiwan has a prevalence of undernourishment of 3·98 %, which is higher than that of some developing countries. As this is not a true reflection of the status of undernourishment in our nation, the purpose of the present study was to modify the FAO methodology for Taiwan.DesignTwo factors were considered in the modified version. As the minimum dietary energy requirement was the main factor contributing to the inflated prevalence in Taiwan, we adjusted for a lighter physical activity level, based on the average BMI of the Taiwanese population, and calculated a new minimum dietary energy requirement. We then fitted a second-order polynomial regression model for prediction of per capita dietary energy supply.ResultsThe adjusted minimum dietary energy requirement was reduced to 7648 kJ/d or 7765 kJ/d compared with the original value of 8054 kJ/d. This resulted in a decrease of the prevalence of undernourishment in Taiwan to 2·5 % or 3·0 %, which is much closer to that of other countries with the same level of economic development. The second-order polynomial regression model efficiently reduced the variation in dietary energy consumption and resulted in an undernourishment prevalence of less than 2·5 %.ConclusionsThis new adapted method is more appropriate for Taiwan. It is recommended that each country evaluates the appropriateness of the FAO approach for its population.

scholarly journals A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1206
Author(s):  
Jungwon Yu ◽  
Soyoung Yang ◽  
Jinhong Kim ◽  
Youngjae Lee ◽  
Kil-Taek Lim ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Confidence Interval ◽  
Process Optimization ◽  
Polynomial Regression ◽  
Optimization Method ◽  
Second Order ◽  
Functional Relationships ◽  
Polynomial Regression Analysis ◽  
Controllable Factors ◽  
Order Polynomial

In the manufacturing processes, process optimization tasks, to optimize their product quality, can be performed through the following procedures. First, process models mimicking functional relationships between quality characteristics and controllable factors are constructed. Next, based on these models, objective functions formulating process optimization problems are defined. Finally, optimization algorithms are applied for finding solutions for these functions. It is important to note that different solutions can be found whenever these algorithms are independently executed if a unique solution does not exist; this may cause confusion for process operators and engineers. This paper proposes a confidence interval (CI)-based process optimization method using second-order polynomial regression analysis. This method evaluates the quality of the different solutions in terms of the lengths of their CIs; these CIs enclose the outputs of the regression models for these solutions. As the CIs become narrower, the uncertainty about the solutions decreases (i.e., they become statistically significant). In the proposed method, after sorting the different solutions in ascending order, according to the lengths, the first few solutions are selected and recommended for the users. To verify the performance, the method is applied to a process dataset, gathered from a ball mill, used to grind ceramic powders and mix these powders with solvents and some additives. Simulation results show that this method can provide good solutions from a statistical perspective; among the provided solutions, the users are able to flexibly choose and use proper solutions fulfilling key requirements for target processes.

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