Academia.eduAcademia.edu

Figure 3 – uploaded by Krišs Spalviņš

See full PDFdownloadDownload figure
The initial optimization model was used to find t mark a point in map with the lowest P;, value (Fig. 3, he optimal area (Fig. 3) and to grey square). By validating the optimization model and replacing the distance of straight lines with real distances via motorways, it was found that the lowest P value deviated by only 7.96 km (Fig. 3., black square) from the initial point with lowest P;, value and the validated optimal location was still within the initially marked area. This indica model has, with very little deviation, indicated the op which also confirms that this model can be used to location. es that the initial optimization imal SCP/SCO plant location, find optimal production plant Figure 3. Coordinates of the optimal location of the production plant, depending on the amount of whey generated in dairy processing companies in Latvia. Initial optimal area (white area) was located by marking 20 coordinates with the lowest P;, values. Grey square — lowest initial P;, value. Lowest validated P;, value (black square) was located by recalculating distances from dairy plants to potential production plant sites inside initial optimal area via motorways. Model XY axis resolution: 1 = {0...106}; z= {0...64}.

Figure 3 The initial optimization model was used to find t mark a point in map with the lowest P;, value (Fig. 3, he optimal area (Fig. 3) and to grey square). By validating the optimization model and replacing the distance of straight lines with real distances via motorways, it was found that the lowest P value deviated by only 7.96 km (Fig. 3., black square) from the initial point with lowest P;, value and the validated optimal location was still within the initially marked area. This indica model has, with very little deviation, indicated the op which also confirms that this model can be used to location. es that the initial optimization imal SCP/SCO plant location, find optimal production plant Figure 3. Coordinates of the optimal location of the production plant, depending on the amount of whey generated in dairy processing companies in Latvia. Initial optimal area (white area) was located by marking 20 coordinates with the lowest P;, values. Grey square — lowest initial P;, value. Lowest validated P;, value (black square) was located by recalculating distances from dairy plants to potential production plant sites inside initial optimal area via motorways. Model XY axis resolution: 1 = {0...106}; z= {0...64}.

Related Figures (3)

Figure 1. Visual example for locating and validating the optimal location for by- processing plant with lowest validated P;, value (white square). A — area of interest on XY axis, with by-product-generating plants (white circles proportional to generated subs coordinates in the area; C — calculated P;, value for single coordinates; D — coord owest P;, value marked in the map (gray square); E — six lowest P;, value mar! ocations will be selected; G — by using Google Maps distance via motorways is rate vo. B — distance measurement using Pythagorean theorem from by-product generator to (2;2 inates with the ked in t produc umes); ne map gray squares); F — six lowest P;,, values used to mark initial optimal area from which populated calculated for plant. every populated location within initial optimal area (gray circles); H — populated location with he lowest P;, value (white square) is the validated optimal location for the by-product processing
Figure 1. Visual example for locating and validating the optimal location for by- processing plant with lowest validated P;, value (white square). A — area of interest on XY axis, with by-product-generating plants (white circles proportional to generated subs coordinates in the area; C — calculated P;, value for single coordinates; D — coord owest P;, value marked in the map (gray square); E — six lowest P;, value mar! ocations will be selected; G — by using Google Maps distance via motorways is rate vo. B — distance measurement using Pythagorean theorem from by-product generator to (2;2 inates with the ked in t produc umes); ne map gray squares); F — six lowest P;,, values used to mark initial optimal area from which populated calculated for plant. every populated location within initial optimal area (gray circles); H — populated location with he lowest P;, value (white square) is the validated optimal location for the by-product processing
Figure 2. The aggregated data on the amount of whey generated by Latvian dairy plants. Circle sizes are proportional to the amount of whey generated in the specific dairy plant (n = 22).
Figure 2. The aggregated data on the amount of whey generated by Latvian dairy plants. Circle sizes are proportional to the amount of whey generated in the specific dairy plant (n = 22).
10 lowest initial P;, values (Fig. 4 . This shows that model can be successfully used even with significantly decreased XY resolution. The decreasing of XY resolution by half did not affect the final result of the optimal SCP/SCO plant location while also significantly decreasing the amoun analysis via MS Excel. With i= { of calculations that are required to perform the 0...106}; z= {0...64} XY resolution, the model required to calculate 6955 P;, values, while with halved resolution i= {0...53}; z= {0...32}, the model required on the amount of required calculations results final results. This shows that y 1782 P;, values to be calculated thus decreasing almost 4 fold, while still providing with the same model can be adjusted for user’s preferences while also making the calculations more manageable. Figure 4. Optimization model repeated with halved XY axis resolution: i= {0...53}; z= {0...32}. Initial optimal area (white area) was located by marking 10 coordinates with the lowest P; , values. Grey square — lowest initial P; , value.
10 lowest initial P;, values (Fig. 4 . This shows that model can be successfully used even with significantly decreased XY resolution. The decreasing of XY resolution by half did not affect the final result of the optimal SCP/SCO plant location while also significantly decreasing the amoun analysis via MS Excel. With i= { of calculations that are required to perform the 0...106}; z= {0...64} XY resolution, the model required to calculate 6955 P;, values, while with halved resolution i= {0...53}; z= {0...32}, the model required on the amount of required calculations results final results. This shows that y 1782 P;, values to be calculated thus decreasing almost 4 fold, while still providing with the same model can be adjusted for user’s preferences while also making the calculations more manageable. Figure 4. Optimization model repeated with halved XY axis resolution: i= {0...53}; z= {0...32}. Initial optimal area (white area) was located by marking 10 coordinates with the lowest P; , values. Grey square — lowest initial P; , value.
Related topics:
Environmental ScienceAgronomyArticlesSingle Cell ProteinDairy Waste-waterAvailability Analysis
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved