Impact of lime application on erosive strength and bulk density of aggregates

The purpose of this study is to analyse the environmental benefits of precision agriculture technology adoption obtained from the mitigation of negative environmental impacts of agricultural inputs in modern farming. Our literature review of the environmental benefits related to the adoption of precision agriculture solutions is aimed at raising farmers' and other stakeholders' awareness of the actual environmental impacts from this set of new technologies. Existing studies were categorised according to the environmental impacts of different agricultural activities: nitrogen application, lime application, pesticide application, manure application and herbicide application. Our findings highlighted the effects of the reduction of input application rates and the consequent impacts on climate, soil, water and biodiversity. Policy makers can benefit from the outcomes of this study developing an understanding of the environmental impact of precision agriculture in order to promote and support initiatives aimed at fostering sustainable agriculture.

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A novel approach to white liquor control in a continuous digester using the Box-Jenkins methodology to predict chip bulk density

TAPPI Journal ◽

10.32964/tj14.6.395 ◽

2015 ◽

Vol 14 (6) ◽

pp. 395-402

Author(s):

FLÁVIO MARCELO CORREIA ◽

JOSÉ VICENTE HALLAK D’ANGELO ◽

SUELI APARECIDA MINGOTI

Keyword(s):

Bulk Density ◽

Moving Average ◽

Pulp Mill ◽

Statistical Decision ◽

White Liquor ◽

Novel Approach ◽

Cooking Process ◽

Eucalyptus Kraft Pulp ◽

Relevant Variables ◽

Kraft Cooking

Alkali charge is one of the most relevant variables in the continuous kraft cooking process. The white liquor mass flow rate can be determined by analyzing the chip bulk density fed to the process. At the mills, the total time for this analysis usually is greater than the residence time in the digester. This can lead to an increasing error in the mass of white liquor added relative to the specified alkali charge. This paper proposes a new approach using the Box-Jenkins methodology to develop a dynamic model for predicting chip bulk density. Industrial data were gathered on 1948 observations over a period of 12 months from a Kamyr continuous digester at a bleached eucalyptus kraft pulp mill in Brazil. Autoregressive integrated moving average (ARIMA) models were evaluated according to different statistical decision criteria, leading to the choice of ARIMA (2,0,2) as the best forecasting model, which was validated against a new dataset gathered during 2 months of operations. A combination of predictors has shown more accurate results compared to those obtained by laboratory analysis, allowing a reduction of around 25% of the chip bulk density error to the alkali addition amount.

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