Multiobjective optimization of parboiled rice quality attributes and total energy consumption

2020 ◽  
Vol 15 (3) ◽  
pp. 24-33
Author(s):  
Mayowa Saheed Sanusi ◽  
Rahman Akinoso
Keyword(s):  
Energy Consumption ◽  
Multiobjective Optimization ◽  
Total Energy ◽  
Quality Attributes ◽  
Total Energy Consumption ◽  
Soaking Time ◽  
Rice Quality ◽  
Parboiled Rice ◽  
Head Rice ◽  
Soaking Temperature

This study was designed to investigate, model and optimize the effect of process factors (soaking temperature, soaking time, steaming time and paddy moisture content) on rice quality attributes and total energy consumption of a commercially grown rice variety (FARO 60) using response surface methodology. The optimum processing conditions obtained for the rice quality attributes and total energy consumption varies from one another. The milling recovery, head rice yield, white bellies, lightness, colour, and total energy consumption values of the parboiled rice ranges from 68.46 - 72.34%; 67.71 - 71.42%; 0.50 - 4.30%; 22.03 - 33.00; 14.10 - 21.21 and 45.27 - 73.68 MJ, respectively. The second-order polynomial models were observed to be fit in predicting milling recovery, head rice yield, white bellies and total energy consumption with the coefficient of determination (R2) that range from 78.71 to 95.03% while colour and lightness values were not fit with R2 ranging from 24.05 to 52.95%. The multiobjective optimization for desirable parboiled rice quality attributes and total energy consumption showed that universal optimum condition was found at 64°C soaking temperature, 11 h soaking time, 35 min steaming time and 17% paddy moisture content. The approach used and information obtained from this study would be useful for rice processors as a strategic means of minimizing total energy consumption, without compromising its desirable quality attributes. Keywords: Multiobjective Optimization; Parboiled Rice Quality Attributes; Total Energy Consumption

scholarly journals The Optimization and Mathematical Modeling of Quality Attributes of Parboiled Rice Using a Response Surface Method

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Khurram Yousaf ◽  
Chen Kunjie ◽  
Chen Cairong ◽  
Adnan Abbas ◽  
Yuping Huang ◽  
...  
Keyword(s):  
Response Surface ◽  
Rice Variety ◽  
Quality Parameters ◽  
Cooking Time ◽  
Soaking Time ◽  
Parboiled Rice ◽  
Surface Method ◽  
Head Rice ◽  
Soaking Temperature

The response surface methodology was used to optimize the hydrothermal processing conditions based on the rice quality parameters of the Rong Youhua Zhan rice variety (Indica). The effect of soaking temperature (29.77, 40, 55, 70, and 80.23°C), soaking time (67.55, 90, 120, 150, and 170.45 min), and steaming time (1.59, 5, 10, 15, and 18.41 min), each tested at five levels, on percentage of head rice yield (HRY), hardness, cooking time, lightness, and color were determined, with R2 values of 0.96, 0.94, 0.90, 0.88, and 0.94, respectively. HRY, hardness, cooking time, and color increased with process severity while lightness decreased, although HRY decreased after reaching a maximum. The predicted optimum soaking temperature, soaking time, and steaming time were 69.88°C, 150 min, and 6.73 min, respectively, and the predicted HRY, hardness, cooking time, lightness, and color under these conditions were 73.43%, 29.95 N, 32.14 min, 83.03 min, and 12.24 min, respectively, with a composite desirability of 0.9658. The parboiling industry could use the findings of the current study to obtain the desired quality of parboiled rice. This manuscript will be helpful for researchers working on commercializing parboiled rice processes in China as well as in other countries.

Energy savings potential of new aeration system: Full scale trials

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
A. Lazić ◽  
V. Larsson ◽  
Å. Nordenborg
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Total Energy ◽  
Energy Savings ◽  
Treatment Plant ◽  
Full Scale ◽  
Total Energy Consumption ◽  
Aeration System ◽  
Fine Bubble ◽  
Aeration Control

The objective of this work is to decrease energy consumption of the aeration system at a mid-size conventional wastewater treatment plant in the south of Sweden where aeration consumes 44% of the total energy consumption of the plant. By designing an energy optimised aeration system (with aeration grids, blowers, controlling valves) and then operating it with a new aeration control system (dissolved oxygen cascade control and most open valve logic) one can save energy. The concept has been tested in full scale by comparing two treatment lines: a reference line (consisting of old fine bubble tube diffusers, old lobe blowers, simple DO control) with a test line (consisting of new Sanitaire Silver Series Low Pressure fine bubble diffusers, a new screw blower and the Flygt aeration control system). Energy savings with the new aeration system measured as Aeration Efficiency was 65%. Furthermore, 13% of the total energy consumption of the whole plant, or 21 000 €/year, could be saved when the tested line was operated with the new aeration system.

scholarly journals Energy Recovery Potential in Industrial and Municipal Wastewater Networks Using Micro-Hydropower in Spain

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 691
Author(s):  
Aida Mérida García ◽  
Juan Antonio Rodríguez Díaz ◽  
Jorge García Morillo ◽  
Aonghus McNabola
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Recovery ◽  
Energy Production ◽  
Municipal Wastewater ◽  
Distribution Networks ◽  
Total Power ◽  
Energy Potential ◽  
Total Energy Consumption ◽  
Recovery Potential

The use of micro-hydropower (MHP) for energy recovery in water distribution networks is becoming increasingly widespread. The incorporation of this technology, which offers low-cost solutions, allows for the reduction of greenhouse gas emissions linked to energy consumption. In this work, the MHP energy recovery potential in Spain from all available wastewater discharges, both municipal and private industrial, was assessed, based on discharge licenses. From a total of 16,778 licenses, less than 1% of the sites presented an MHP potential higher than 2 kW, with a total power potential between 3.31 and 3.54 MW. This total was distributed between industry, fish farms and municipal wastewater treatment plants following the proportion 51–54%, 14–13% and 35–33%, respectively. The total energy production estimated reached 29 GWh∙year−1, from which 80% corresponded to sites with power potential over 15 kW. Energy-related industries, not included in previous investigations, amounted to 45% of the total energy potential for Spain, a finding which could greatly influence MHP potential estimates across the world. The estimated energy production represented a potential CO2 emission savings of around 11 thousand tonnes, with a corresponding reduction between M€ 2.11 and M€ 4.24 in the total energy consumption in the country.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

Optimizing the production scheduling of a single machine to minimize total energy consumption costs

2014 ◽  
Vol 67 ◽  
pp. 197-207 ◽  
Author(s):  
Fadi Shrouf ◽  
Joaquin Ordieres-Meré ◽  
Alvaro García-Sánchez ◽  
Miguel Ortega-Mier
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Single Machine ◽  
Production Scheduling ◽  
Total Energy Consumption
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