Recovered Paper Yield Estimate Recovered Paper Yield Estimate Using Laboratory + Mill Using Laboratory + Mill Accounting Data Data Accounting By: By: Robert de de Jong Jong 2 Fiber Consulting Fiber Consulting Robert 2
Objective Objective � To get maximum quantity of RP to the PM To get maximum quantity of RP to the PM � headbox without affecting product quality headbox without affecting product quality � Remove contaminants: Remove contaminants: � � Metal, plastic, wet strength, stickies Metal, plastic, wet strength, stickies � � Remove undesirable components: Remove undesirable components: � � Such as ash, RP fines, solubles Such as ash, RP fines, solubles � � Minimize losses of good fibers from process, Minimize losses of good fibers from process, � which will increase residue disposal costs which will increase residue disposal costs � Track RP yield to control process economics Track RP yield to control process economics � 2 2
Introduction Introduction � Yield = (output tons / input tons) % Yield = (output tons / input tons) % � � “ “As is As is” ” or bone dry (BD) basis or bone dry (BD) basis � � Accounting data subject to errors: Accounting data subject to errors: � � Incorrect scale weights Incorrect scale weights � � Inventory differences Inventory differences � � Broke accounting discrepancies Broke accounting discrepancies � � Yield per RP Grade not identified Yield per RP Grade not identified � 3 3
Hypothetical Mill Example Hypothetical Mill Example � Produces Produces 21,000 21,000 “ “as is as is” ” tons / month tons / month � � Furnish = Furnish = 30,000 30,000 “ “as is as is” ” tons / month tons / month � � SWL SWL = 20% 20% = � � CBK CBK = 20% 20% = � � ONP ONP = 20% 20% = � � Wetlap Pulp Wetlap Pulp = 20% at PM 20% at PM = � � Broke Broke = 20% at PM 20% at PM = � Numbers in Blue are Spreadsheet Variables Numbers in Blue are Spreadsheet Variables 4 4
Assumed Moisture Content Assumed Moisture Content 60% 50% 50% Moisture % MOISTURE 40% content 30% 20% 9% 7% 7% 10% 6% 6% 0% SWL CBK ONP Wetlap Broke Product The 50% moisture content of Wetlap lowers yield The 50% moisture content of Wetlap lowers yield 5 5
“As is As is” ” Yield Yield “ Tons/mo. Moist. Tons/mo. Moist. As is Furnish Comp. . As is Furnish Comp As is Cont. As is Cont. Input DP = 20% 20% SWL SWL 6,000 6% Input DP = 6,000 6% 20% CBK CBK 6,000 6% 20% 6,000 6% 20% ONP ONP 6,000 9% 20% 6,000 9% Input PM= 20% 20% Wetlap Wetlap 6,000 50% Input PM= 6,000 50% 20% Broke Broke 6,000 7% 20% 6,000 7% TOTAL INPUT = 30,000 TOTAL INPUT = 30,000 OUTPUT = 21,000 OUTPUT = 21,000 7% 7% LOSSES = 9,000 LOSSES = 9,000 YIELD = 21,000/30,000 = 70% 70% YIELD = 21,000/30,000 = 6 6
Recalculated on BD Basis Recalculated on BD Basis Tons/mo. Tons/mo. BD Furnish Comp. . BD BD Furnish Comp BD Input DP = 22.3% SWL 22.3% SWL 5,640 Input DP = 5,640 22.3% CBK 5,640 22.3% CBK 5,640 22.6% ONP 5,460 22.6% ONP 5,460 Input PM= 11.8% Wetlap 3,000 Input PM= 11.8% Wetlap 3,000 22.0% Broke 5,580 22.0% Broke 5,580 TOTAL INPUT = 25,320 TOTAL INPUT = 25,320 OUTPUT = 19,530 OUTPUT = 19,530 LOSSES = 5,790 LOSSES = 5,790 YIELD = 19,530 / 25,320 = 77.1% 77.1% YIELD = 19,530 / 25,320 = 7 7
Typical Average Laboratory Data Typical Average Laboratory Data RP Grade RP Grade 6 Cut Rej. . Solubles BJ F&A 6 Cut Rej Solubles BJ F&A SWL 0.2 % 4 % 26 % SWL 0.2 % 4 % 26 % CBK 0.4 % 6 % 33 % CBK 0.4 % 6 % 33 % ONP 1.4 % 1 % 27 % ONP 1.4 % 1 % 27 % Wetlap Pulp 0 % 2.5 % 20 % Wetlap Pulp 0 % 2.5 % 20 % Broke 0.1 % 1 % 11 % Broke 0.1 % 1 % 11 % Fin. Product 0 % 1 % 11 % Fin. Product 0 % 1 % 11 % Numbers in Blue are Spreadsheet Variables Numbers in Blue are Spreadsheet Variables 8 8
Allocating Coarse Rejects Allocating Coarse Rejects � Coarse Rejects to landfill = Coarse Rejects to landfill = 180 180 BD tons / mo BD tons / mo � (according to accounting data) (according to accounting data) � Good fiber content = Good fiber content = 30 30% % � (according to Lab) (according to Lab) � Allocate Coarse Rejects Allocate Coarse Rejects Input Input � (% 6 cut rejects x BD RP tons + 30 30%) %) (% 6 cut rejects x BD RP tons + � Compare with accounting data Compare with accounting data � � Distribute unaccounted tons Distribute unaccounted tons � Numbers in Blue are Spreadsheet Variables Numbers in Blue are Spreadsheet Variables 9 9
6 Cut Rejects in Furnish + Product 6 Cut Rejects in Furnish + Product (BD tons/mo) (BD tons/mo) RP Lab 30% RP Lab 30% Grade Calc. Fiber Unacc. Losses Grade Calc. Fiber Unacc. Losses SWL 11 + 3 + 3 = 17 SWL 11 + 3 + 3 = 17 CBK 23 + 7 + 6 = 35 CBK 23 + 7 + 6 = 35 ONP 76 + 23 + 19 = 119 ONP 76 + 23 + 19 = 119 Wetlap 0 Wetlap 0 Broke 6 + 2 + 1 = 9 Broke 6 + 2 + 1 = 9 Total 116 + 35 + 29 = 180 Total 116 + 35 + 29 = 180 Product 0 Product 0 30% good fiber per lab, 180 tons per accounting 30% good fiber per lab, 180 tons per accounting 10 10
Allocating Solubles Allocating Solubles � Calculate input solubles Calculate input solubles � (% solubles x BD RP tons ) (% solubles x BD RP tons ) � Calculate solubles in Product Calculate solubles in Product � (% solubles x BD Product tons ) (% solubles x BD Product tons ) � Allocate output solubles Allocate output solubles � (according to BD furnish composition) (according to BD furnish composition) � Input Input – – output tons = solubles per furnish output tons = solubles per furnish � component component 11 11
Solubles in Furnish + Product Solubles in Furnish + Product (BD tons/mo) (BD tons/mo) RP Lab Tons RP Lab Tons Grade Calc. In Prod Losses Grade Calc. In Prod Losses SWL 226 - - 44 = 182 SWL 226 44 = 182 CBK 338 - - 44 = 295 CBK 338 44 = 295 ONP 55 - - 42 = 13 ONP 55 42 = 13 Wetlap 75 - - 23 = 52 Wetlap 75 23 = 52 Broke 56 - - 43 = 13 Broke 56 43 = 13 195 554 195 554 Product 195 Product 195 12 12
Calculate BJ Fines & Ash Calculate BJ Fines & Ash in PM Furnish and DIP in PM Furnish and DIP Assume: Assume: � Wetlap + Broke F & A retained Wetlap + Broke F & A retained 90% 90% � (determine when deink plant is down) (determine when deink plant is down) � Calculate F & A in Product Calculate F & A in Product � � Calculate F & A from wetlap + broke Calculate F & A from wetlap + broke � � Difference is from DIP Difference is from DIP � � Allocate DIP F & A Allocate DIP F & A � (according to DIP BD furnish) (according to DIP BD furnish) 13 13
BJ F & A in PM Furnish + Product BJ F & A in PM Furnish + Product (BD tons/mo) (BD tons/mo) RP Lab Tons in BJ RP Lab Tons in BJ Grade Calc. Product Losses Grade Calc. Product Losses DIP 1,056 DIP 1,056 Wetlap 600 - - 540* = 60 Wetlap 600 540* = 60 Broke 614 - - 552* = 61 Broke 614 552* = 61 1,092 1,092 Product 2,148 2,148 Product 2,148 2,148 *=90% PM fines & ash retained; rest is removed *=90% PM fines & ash retained; rest is removed 14 14
BJ F & A in DIP BJ F & A in DIP (BD tons/mo) (BD tons/mo) RP Lab Tons in Sub- - BJ RP Lab Tons in Sub BJ Grade Calc. Product Total Losses Grade Calc. Product Total Losses SWL 1,466 - - 356 = 1,111 SWL 1,466 356 = 1,111 CBK 1,861 - - 356 = 1,505 CBK 1,861 356 = 1,505 ONP 1,474 - - 344 = 1,130 ONP 1,474 344 = 1,130 1,056* 1,056* Wetlap 600 - - 540 = 60 Wetlap 600 540 = 60 Broke 614 - - 552 = 61 Broke 614 552 = 61 1,092 3,867 1,092 3,867 2148 - 2148 -1092 = 1056 in DIP 1092 = 1056 in DIP 15 15
Calculate Residue Losses Calculate Residue Losses Assume: Assume: � Residue contains Residue contains 20% 20% good fiber good fiber � (according to lab samples) (according to lab samples) � Accounting reports Accounting reports 4,900 4,900 tons BD residue tons BD residue � (according to truck weights x average dry (according to truck weights x average dry solids content of residue) solids content of residue) � Add BJ losses + Add BJ losses + 20% 20% fiber losses fiber losses � � Compare with accounting BD residue losses Compare with accounting BD residue losses � � Distribute unaccounted losses Distribute unaccounted losses � 16 16
Recommend
More recommend
