(404b) Atatad Heat Accumulation in Countercurrent Flow, Continuous Phase Sludge Treatment Process to Class a Fertilizer | AIChE

(404b) Atatad Heat Accumulation in Countercurrent Flow, Continuous Phase Sludge Treatment Process to Class a Fertilizer

Authors 

Johnson, S. - Presenter, The Pennsylvania State University
This document describes reduction to experimental practice, for the first time, for a patented method of pasteurization that converts wastewater sludge into Class A fertilizer using air in a continuous process. Abbreviated atATAD for atmospheric air thermophilic aerobic digestion, the process represents a paradigm shift in sludge treatment from batch to continuous processing that utilizes a multi-stage countercurrent setup to achieve pasteurization temperatures within prescribed residence time. Beyond validating the patent’s concept, atATAD was accomplished with less than half the patents’ modeled solids content (4-5%). Intensive real-time data collection and post-run sample processing using CHN analysis and bomb calorimetry were used to begin definition of the parameters for a dynamic model. Specifically, a single-stage proxy for the full multi-stage countercurrent system was run as a batch reaction in an attempt to establish the mass and energy balances; the batch runs reached pasteurization temperatures but did not close the mass and energy balance. This initial success of reaching thermophilic temperatures motivated a pilot-scale (1650 gallons) continuous phase reaction with 4-day residence time. This comprehensive pilot-scale demonstration conducted on-site at the local wastewater treatment authority, included far more extensive data sets and verified the use of air and countercurrent flow to yield pasteurization conditions. The goal of biohazard reduction from pasteurization temperatures exceeding 50 °C through aerobic consumption of solids is met.

The rapid transition to a real-world pilot implementation introduced significant operational constraints that prevented steady operation. None-the-less, the resulting dynamic data represents an opportunity which dynamic modeling may be fit to in the future. Dynamic fitting with the continuous temperature data from varying, inevitable condition changes (composition, amount of solids, plugs to the system, energy potential, etc).