Energy efficiency in processing of natural raw materials under consideration of uncertainties

Abstract The processing of natural resources involves many variations of uncertainties as the natural resources themselves vary in their composition. Since the weather influences harvest quality, natural products are strongly affected by weather conditions. Excessive rainfall increases the water content of products, while a lack of rain may cause the whole harvest to dry out. Due to the varying soil conditions also the quality of wheat, measured, for example, by its protein or starch content, varies from region to region. During the processing of natural products, as presented in this case study for animal feed, the produced compound feed can change its composition as well, for example by raising the starch content through the addition of hot steam to wheat grains. As the processing of natural raw materials as compound feed consumes a lot of energy, producers aim to decrease costs related to energy consumption without decreasing the product's quality. In addition, energy efficient production also leads to lower CO 2 emissions. This paper highlights the energy efficiency challenges during the processing of natural resources in feed processing and gives advice on how to cope with uncertainties by reaching the goal of achieving a constant product quality.

[1]  Nathan Pelletier,et al.  Life cycle assessment of high- and low-profitability commodity and deep-bedded niche swine production systems in the Upper Midwestern United States , 2010 .

[2]  C. Sundberg,et al.  Uncertainties in the carbon footprint of refined wheat products: a case study on Swedish pasta , 2011 .

[3]  Bo Pedersen Weidema,et al.  Increasing credibility of LCA , 2000 .

[4]  Mark A. J. Huijbregts,et al.  Application of uncertainty and variability in LCA , 1998 .

[5]  Jinglan Hong,et al.  Uncertainty propagation in life cycle assessment of biodiesel versus diesel: global warming and non-renewable energy. , 2012, Bioresource technology.

[6]  C. Weber Uncertainty and Variability in Product Carbon Footprinting , 2012 .

[7]  G. Psacharopoulos Overview and methodology , 1991 .

[8]  Andreas Ciroth,et al.  Cost data quality considerations for eco-efficiency measures , 2009 .

[9]  Risto Soukka,et al.  Uncertainty and Sensitivity in the Carbon Footprint of Shopping Bags , 2011 .

[10]  Lauren Basson,et al.  An integrated approach for the consideration of uncertainty in decision making supported by Life Cycle Assessment , 2007, Environ. Model. Softw..

[11]  Dirk P. Kroese,et al.  Handbook of Monte Carlo Methods , 2011 .

[12]  J. Kamphues,et al.  Lower grinding intensity of cereals for dietetic effects in piglets , 2007 .

[13]  G. Mudd Global trends in gold mining: Towards quantifying environmental and resource sustainability , 2007 .

[14]  Robert Ries,et al.  Characterizing, Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches , 2007 .

[15]  Mark A. J. Huijbregts,et al.  Framework for modelling data uncertainty in life cycle inventories , 2001 .

[16]  Bo Pedersen Weidema,et al.  Data quality management for life cycle inventories—an example of using data quality indicators☆ , 1996 .

[17]  Guido Sonnemann,et al.  Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator , 2003 .

[18]  Per-Anders Hansson,et al.  Uncertainties in the carbon footprint of food products: a case study on table potatoes , 2010 .

[19]  W. Winiwarter,et al.  Uncertainties in greenhouse gas emission inventories — evaluation, comparability and implications , 2001 .

[20]  Shang-Lien Lo,et al.  Quantifying and reducing uncertainty in life cycle assessment using the Bayesian Monte Carlo method. , 2005, The Science of the total environment.

[21]  Andreas Ciroth Uncertainty calculation for LCI data: Reasons for, against, and an efficient and flexible approach for doing it , 2004 .

[22]  Reinout Heijungs,et al.  Identification of key issues for further investigation in improving the reliability of life-cycle assessments , 1996 .

[23]  Reinout Heijungs,et al.  A review of approaches to treat uncertainty in LCA , 2004 .

[24]  Maurizio Cellura,et al.  Life cycle assessment of Italian citrus-based products. Sensitivity analysis and improvement scenarios. , 2010, Journal of environmental management.

[25]  Helmut Rechberger,et al.  Practical handbook of material flow analysis , 2003 .

[26]  David E. Clay,et al.  The life cycle impacts of feed for modern grow-finish Northern Great Plains US swine production. , 2012 .

[27]  Konrad Hungerbühler,et al.  Uncertainty analysis in life cycle inventory. Application to the production of electricity with French coal power plants , 2000 .

[28]  G. Lardy Feeding Coproducts of the Ethanol Industry to Beef Cattle , 2007 .

[29]  Klaus-Dieter Thoben,et al.  Manufacturing with Minimal Energy Consumption: A Product Perspective , 2014 .

[30]  Valerio Lo Brano,et al.  Life cycle assessment of a solar thermal collector: sensitivity analysis, energy and environmental balances , 2005 .

[31]  Maurizio Cellura,et al.  Sensitivity analysis to quantify uncertainty in Life Cycle Assessment: The case study of an Italian tile , 2011 .

[32]  Andreas Ciroth,et al.  Uncertainty calculation in life cycle assessments , 2004 .

[33]  David Evans,et al.  How LCA studies deal with uncertainty , 2002 .