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1.
The contents of Cu, Mn, Zn, Cd, Cr, and Pb at different stages of the winemaking process
Janja Kristl, Marjan Veber, Metka Slekovec, 2003, original scientific article

Abstract: In samples taken during different stages of winemaking process (from grapes, crushed grapes, pressed pomace, must deposit, deposit of lees, must before and after clarification and wine) the Cu, Mn and Zn contents were determined by flame atomic absorption spectrometry (FAAS) and the Cd, Pb and Cr contents were determined by electrothermal atomic absorption spectrometry (ETAAS). Grapes, crushed grapes, pressed pomace, must deposit and deposit of lees were microwave digested with nitric acid, hydrofluoric acid and hydrogen peroxide solution, while for must and wine no special treatments were necessary. The highest contents of Cu, Mn, Zn, Pb, Cd and Cr were determined in the pressed pomace, lees, and in white grape varieties also in must deposit. Mean values obtained of dry weight (pressed pomace, lees, must deposit) were 63 mg/kg, 300 mg/kg, 184 mg/kg for Cu, 11 mg/kg, 15 mg/kg, 134 mg/kg for Mn, 14 mg/kg, 35 mg/kg, 17 mg/kg for Zn, 0.3 mg/kg, 0.5 mg/kg, 0.6 mg/kg for Pb, 0.5 mg/kg, 1.0 mg/kg, 1.8 mg/kg for Cr, 15.4 µg/kg, 24.4 µg/kg, 13.0 µg/kg for Cd. The Cu content was decreasing from the grapes to the bottled wine, whereas the Mn, Zn, Cd, Cr and Pb contents in the bottled wine were higher than in musts in all investigated white and red grape varieties. In ten wine samples the following contents were determined: Cu; mean 0.12 mg/L (range: 0.06-0.30 mg/L), Mn; mean 1.04 mg/L (range: 0.60-1.78 mg/L), Zn; mean 0.50 mg/L (range: 0.13-1.03 mg/L), Cd; mean 0.34 µg/L (range: 0.08-1.04 µg/L), Cr; mean 17.0µg/L (range: 5.2-25.1 µg/L) and Pb; mean 25.3 µg/L (range: 16.4-37.8 µg/L).
Keywords: viticulture, chemical processes, chemical elements, chemical analysis
Published in DKUM: 10.07.2015; Views: 1665; Downloads: 130
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Multi-criteria optimization in a methanol process
Anita Kovač Kralj, Peter Glavič, 2009, original scientific article

Abstract: Opportunities for additional profit in retrofits depend very much on the existing plant structure, its parameters and energy system. Combined production of heat flow rate, power and chemical products can improve process efficiency. This paper presents an application of the nonlinear programming (NLP) optimization techniques, including increased chemical product output, heat integration and electricity cogeneration by changing amount flow ratios of raw material, and modifying the separation and reaction systems. The existing NLP model has been extended with basic chemical kinetics, including the effects of changing raw material flow rate ratios on product yield. A case studied methanol plant was optimized using the NLP model developed earlier by including an additional flow rate of hydrogen (H2), decreasing flowrate of high-pressure steam in crude methanol recycling, and increasing methanol production by 2.5%. The potential additional profit from the cogeneration and additional methanol production was estimated to be 2.51 MEUR/a.
Keywords: chemical processes, methanol, simultaneous optimization, NLP, cogeneration, flow rate ratios
Published in DKUM: 01.06.2012; Views: 1949; Downloads: 105
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4.
Optimization of a gas turbine in the methanol process, using the NLP model
Anita Kovač Kralj, Peter Glavič, 2007, original scientific article

Abstract: Heat and power integration can reduce fuel usage, CO2 and SO2 emissions and, thereby, pollution. In the simultaneous heat and power integration approach and including additional production, the optimization problem is formulated using a simplified process superstructure. Nonlinear programming (NLP) contains equations which enable structural heat and power integration and parametric optimization. In the present work, the NLP model is formulated as an optimum energy target of process integration and electricity generation using a gas turbine with a separator. The reactor acts as a combustion chamber of the gas turbine plant, producing high temperature. The simultaneous NLP approach can account for capital cost, integration of combined heat and power, process modification, and additional production trade-offs accurately, and can thus yield a better solution. It gives better results than non-simultaneous methods. The NLP model does not guarantee a global cost optimum, but it does lead to good, perhaps near optimum designs. This approach is illustrated by an existing, complex methanol production process. The objective function generates a possible increase in annual profit of 1.7 M EUR/a.
Keywords: chemical processes, methanol, simultaneous optimization, NLP, cogeneration, gas turbine
Published in DKUM: 31.05.2012; Views: 2110; Downloads: 91
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