Comparative description of coal feeding systems for fixed bed pressure gasification

Coal feeding systems are discussed which are capable of feeding 20-100 T/H and the range of pressure is up to 100 bar. Most emphasis is placed on dry feeding systems. The systems outlined are subdivided into continuous and intermittent and the influence of each system on lock gas losses and reactor design is shown. Finally a cost estimate is presented which indicates the areas of preferred application and permits conclusions to be drawn regarding the economics of the various systems

Protein content of concentrates for turkeys

Cover title.Includes bibliographical references

Factors influencing hatchability in the domestic fowl

Cover title

Factors influencing production of clean eggs

Cover title

Poultry industry of Missouri

Cover title.Includes bibliographical references (page 101)

Seasonal variation in egg quality

The bulletin is a report on Department of Poultry Husbandry research project 17, Poultry Losses--P. [2].Includes bibliographical references (pages 21-[24])

Pasteurization of shell eggs

Publication authorized April 27, 1943.Includes bibliographical references (page 28)

Experiments in cleaning soiled eggs for storage

Digitized 2007 AES.Includes bibliographical references (page [36])

Chicago Board of Trade Ethanol Contract Efficiency

Firms producing ethanol may find management of the price risk associated with production of this leading alternative fuel a key factor to continued success. As with other agricultural commodities, the influence and ability of futures contracts to serve as a risk management tool deserves attention.contract efficiency, ethanol, futures contracts, Crop Production/Industries, Risk and Uncertainty, Q13, Q43, M31,

TVL₁shape approximation from scattered 3D data

With the emergence in 3D sensors such as laser scanners and 3D reconstruction from cameras, large 3D point clouds can now be sampled from physical objects within a scene. The raw 3D samples delivered by these sensors however, contain only a limited degree of information about the environment the objects exist in, which means that further geometrical high-level modelling is essential. In addition, issues like sparse data measurements, noise, missing samples due to occlusion, and the inherently huge datasets involved in such representations makes this task extremely challenging. This paper addresses these issues by presenting a new 3D shape modelling framework for samples acquired from 3D sensor. Motivated by the success of nonlinear kernel-based approximation techniques in the statistics domain, existing methods using radial basis functions are applied to 3D object shape approximation. The task is framed as an optimization problem and is extended using non-smooth L1 total variation regularization. Appropriate convex energy functionals are constructed and solved by applying the Alternating Direction Method of Multipliers approach, which is then extended using Gauss-Seidel iterations. This significantly lowers the computational complexity involved in generating 3D shape from 3D samples, while both numerical and qualitative analysis confirms the superior shape modelling performance of this new framework compared with existing 3D shape reconstruction techniques