Date of Award

2017

Document Type

Open Access Dissertation

Department

Civil and Environmental Engineering

Sub-Department

College of Engineering and Computing

First Advisor

Nicole D. Berge

Abstract

Hydrothermal carbonization (HTC) is a novel environmentally beneficial thermal conversion process for the transformation of organic feedstocks to value-added products. However, little is known about the role of feedstock properties and/or process conditions during carbonization. This study was conducted to determine the parameters most influential during the HTC of organic feedstocks. Experiments and statistical analyses were conducted to: (1) determine the effect of specific feedstocks, feedstock interactions, and process conditions on carbonization product characteristics; (2) understand how initial liquid characteristics influence product characteristics and evaluate the significance of these initial liquid characteristics in predicting product characteristics; and (3) develop statistical models to predict product characteristics and determine parameter influence on carbonization product characteristics when carbonizing various feedstocks at different reaction conditions. Results from laboratory-scale experiments evaluating the carbonization of food waste and packaging materials indicate solid concentration influences carbon distribution. The presence of packaging materials significantly influences hydrochar carbon content. Laboratory-scale experiment results from the carbonization of wastes in the presence of different initial liquids suggest activated sludge and landfill leachate impart minimal impact on the evaluated carbonization product characteristics. Multiple linear regression and regression tree models were developed and indicate process conditions are more influential to the hydrochar yield, liquid and gas-phase carbon content, while feedstock proximate and ultimate properties are more influential on hydrochar carbon, energy contents, and the normalized carbon content of the solid. Additional linear and nonlinear (e.g., regression tree and random forest) models were developed with a larger number of feedstock properties to describe hydrochar yield, carbon content, and energy content. Results from Sobol analysis of these models suggests the most influential parameters to hydrochar yield are solid concentration, temperature, feedstock lignin, polarity, hydrogen, carbon, time and ash. The most influential parameters to hydrochar carbon content are feedstock hydrogen, carbon, solid concentration and ash or volatile matter. The most influential parameters to hydrochar energy content are feedstock hydrogen, carbon, oxygen, ash, temperature and time. These most influential feedstock properties should be considered during feedstock selection. Overall, results from this work provide models that can be used to predict carbonization product characteristics.

Rights

© 2017, Liang Li

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