Cours d'économie industrielle, 1836-[1839] ... Leçons ...

Vol. 3 published by L. Hachette.

A ministry of twenty years in Ann Arbor, Michigan : sermons and pamphlets /

V. 2. A ministry of fifteen years ... -- The door of new opportunity ... -- Two sermons -- Rev. B. Fay Mills and the State University -- Who are saved? -- Concerning prayer -- Religious insincerity -- Robert Ingersoll -- Thomas Paine -- Talmage as a sign ... -- Ralph Waldo Emerson -- Need a traveller drink wine? -- Christian missions in India -- Dr. Winchell's "preadamites" -- The Bible / Eliza R. Sunderland -- Miracles / Eliza R. Sunderland -- God / E.R. Sunderland -- Thomas Hill Green / by Eliza R. Sunderland -- Dr. Martineau's "Study of religion" / Eliza R. Sunderland -- Hon. James M. Ashley.

Prodromus floræ Nepalensis sive Enumeratio vegetabilium quæ in itinere per Nepaliam proprie dictam et regiones conterminas, ann. 1802- 1803 /

Mode of access: Internet.

A statistical view of the number of sheep in the several towns and counties in Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, Pennsylvania, and Ohio: a partial account of the number of sheep in Virginia, Maryland, New Jersey, Delaware, and Kentucky in 1836. And an account of the principal woollen manufactories in said states.

Microfilm. Ann Arbor, Mich., University Microfilms [n.d.] (American Culture Series, reel 470.9)

Lives of the three Mrs. Judsons : Mrs. Ann H. Judson, Mrs. Sarah B. Judson, Mrs. Emily C. Judson, missionaries to Burmah /

Mode of access: Internet.

W.S. Perry School (A Study)

Architectural rendering showing addition to W.S. Perry School. Image from publication: The School Building Programs of Ann Arbor, Michigan ... , Ann Arbor, Michigan Board of Education, 1922.

Ann Arbor. Architect's School, U of M

Emil Lorch & Associates, architect. Built in 1928. Architecture Building; later called Architecture & Design; renamed Lorch Hall ca. 1980. The Doric columns were once part of the oldest stone building in Detroit, the Bank of Michigan, built 1836. The Corinthian column was from the Home Office Bldg. of the Mutural Benefit Life Insurance Co. of Newark, N.J.

Perry School

Postmark dated. Perry School before it was enlarged. Doorway in view faced on the small park in which children were not allowed to play. This was the the girls entrance. Boys entered doorway on Packard. Sidewalk leading to it visible in picture.

A testimony concerning William Byrd : and one concerning Ann Brewster.

Bound with Smith, Edward. A letter addressed to the members of the Society of Friends. London, 1833.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.