Project Methodology

Feedstock characterization 

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The project's methodology began with data collection to determine the key characteristics of the feedstock. This feedstock consisted primarily of cow manure, with distillery waste as a co-substrate. Characterization included the following:total Solids (TS) content,  volatile Solids (VS) content and chemical composition (approximate percentages of carbohydrates, proteins, and fats).
This data was followed by Aspen Input, which involved determining the optimal way to represent the feedstock within Aspen Plus. This included deciding whether to use existing components or create custom components if the necessary ones were not available in the Aspen database.

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Anaerobic Digestion Model Development 

 

Aspen Plus offers a variety of reactor choices, but selecting the one that best aligns with the anaerobic digestion (AD) process is crucial for maximizing output and balancing cost and efficiency. For this process, a plug flow reactor was the optimal choice due to the high solids content in our feedstock, which would hinder the performance of a CSTR. Additionally, a plug flow reactor allows us to model variations in concentration and temperature along the reactor's length. Following reactor selection, we chose the appropriate kinetic model to simulate biochemical reactions and defined the operating conditions such as temperature, pH, and pressure.

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 Simulation Setup

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The simulation step involves developing the optimal process arrangement within the simulation environment. This primarily consists of connecting the unit operations and defining the input streams.  Crucially, this stage includes determining the best ratio of cow manure to distillery waste.  This optimized ratio aims to achieve the best results while creating a suitable environment for the microorganisms, preventing any system toxicity.

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 Data Analysis

 

The simulation results were validated by comparing them to findings from relevant papers identified in a literature review. The analysis focused on whether the predicted biogas yield and composition fell within the expected ranges based on the literature.  Importantly, these comparisons were made with papers investigating feedstocks that shared similar properties. Additionally, insights from the literature review were used to calculate the energy content of the simulated green gas.

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