Research Faculty/Areas of Expertise
Javad Abbasian (ChBE): biomass gasification, process design, utilization of municipal wastewater for cooling in thermoelectric plants
Paul Anderson (CAEE): water and wastewater, resource recovery, water quality modeling
Hamid Arastoopour (ChBE/MMAE): utilization of municipal wastewater for cooling in thermoelectric power power plants
Carrie Hall (MMAE): utilization of biofuel
Adam Hock (CHEM): materials for photovoltaics
Seok Hoon Hong (ChBE): bioenergy
Omar Khalil (ChBE): bioenergy
Nasrin Khalili: evaluation of water footprints
Ali Khounsary (PHYS): energy efficiency, bioenergy
Sohail Murad (ChBE): bioenergy
Satish Parulekar (ChBE): bioenergy
Francisco Ruiz (MMAE): combustion of bioenergy
Fouad Teymour (ChBE): bioenergy
Selected Current Projects
1. Studies on Efficient Engines Using Renewable Fuels (PI: Carrie Hall, MMAE)
This projects examines methods of enabling efficient and clean use of renewable fuels in internal combustion engines. Some more advanced engines that leverage more complex combustion processes have been shown to be able to achieve high efficiencies and low emissions when using alternative fuels. This group at IIT focuses on studying the dynamics of such high efficiency engines operating with renewable fuels and developing control methodologies that can enable them to be viable in production vehicles.
2. Development of an Artificial Intelligence-Based Biomass Gasification Model (PI: Javad Abbasian, ChBE)
To get a firmer grasp on our ecosystem, mitigate global warming effects, and build a sustainable future we need to invest in becoming more efficient in utilizing renewable resources such as biomass; mainly due to its life cycle carbon-neutrality and the potential to substitute fossil fuel to produce a variety of energy-related products. A broad range of organic carbon-containing material such as agriculture and forestry residue, negative value feedstocks such as coke and municipal solid waste can be converted into different products through various thermal conversion processes.
This research focuses on developing a general mathematical model to accurately predict the properties of the syngas produced in gasification of biomass in fluidized bed reactors. Our approach is based on employing artificial intelligence and machine learning techniques to systematically quantify collective impact of different critical factors in the process. This approach generally involves, characterization of the solid fuel, identification of multiple measures of associations between biomass constituents, and the operating condition. Such a model will allow one to optimize the blend of biomass fuel and operating conditions to meet desirable specifications for various applications of interest including power, hydrogen, methanol and Fischer-Tropsch products.
3. Molecular Modeling of Desalination (PI: Sohail Murad, ChBE)
Molecular simulation methods such as molecular dynamics allow screening potential membranes for their ability to prevent the permeation of various ions (including Na+ and Cl- which are of interest in desalination). With support from a grant from the National Science Foundation (CBET 1545560), we recently completed several studies to examine a range of zeolite membranes for their ability to remove ions found as water pollutants including : monovalent alkali ions (Li+, Na+, K+, Rb+, Cs+), bivalent alkaline earth ions (Mg2+, Ca2+, Sr2+, Ba2+), ions from various groups (Ag+, V2+, Fe2+, Zn2+, Al3+, Ti3+, Fe3+, Cr3+). These studies permit choosing the most suitable membranes for removing selected pollutants from water.
4. Smart Sensors and Applications of Big Data to Water Resources (PI: Paul Anderson, CAEE)
Soft sensors are mathematical or statistical approaches for predicting information from data that are historical, readily-acquired, and/or anticipated (such as weather forecasts). Soft sensors are attractive because they are relatively low cost, they have fast response times, and they can be used in parallel or integrated with hard-sensors to enhance the reliability of process control. Soft sensors do not have to take the place of conventional hard sensors; they can complement and add value to the existing information. Additional benefits of soft sensors include the ability to predict future information, the capacity for learning and communication, and the ability to identify errors in the data. In this work we are examining how soft sensors and data analytics can be applied to wastewater treatment processes to develop an intelligent process management system, which integrates soft sensors, process controls, and energy efficiency evaluation, to help reduce operating (energy) costs, maintain effluent quality, and improve process resilience.
5. An Assessment of Waste Heat Recovery from Municipal Sewer Systems (PI: Paul Anderson, CAEE)
A substantial amount of thermal energy leaves industrial, commercial, and residential facilities with their wastewater. In this project we are assessing energy distribution in a wastewater collection system to develop a model of the spatial and temporal distribution of heat, and identify the most promising sites for installation of wastewater source heat pumps for energy recovery. The assessment will consider capital and operating costs and greenhouse gas reductions for space heating, hot water production, and space cooling.
6. Bioaugmentation Technology for Wastewater Pretreatment (PI: Paul Anderson, CAEE)
IIT is collaborating with In-Pipe Technology (IPT) LLC to further develop their bioaugmentation service. The IPT approach transforms a wastewater collection system into a series of biological reactors, increasing the overall treatment efficiency and reducing energy consumption in the wastewater treatment process. The service takes advantage of suitable environmental conditions in the collection system and adds beneficial bacteria along the collection system. The bioaugmentation approach makes it possibel to treat contaminants prior to the wastewater treatment plant.
7. The Role of Bayesian Networks in More Effective Watershed Management (PI: Paul Anderson, CAEE)
The objective of this work is to develop a framework to improve the utility of watershed and water quality models with a Bayesian Network that explicitly incorporates uncertainty into the model to improve communications between model developers and other stakeholders in the watershed. The work includes a critical review of existing studies on the total maximum daily load (TMDL) to determine how the margin of safety (MOS) depends on waterbody and watershed size, watershed population, impairment type, and the designated use of the water body. In addition, we will assess potential relationships between the MOS value and factors such as the cost of TMDL implementation and the expected water quality. Ultimately, we hope to develop an approach for estimating a risk-based MOS for watershed studies based on the aforementioned factors.
8. Investigation of Metal-Organic Framework Compounds for an Arsenic Sensor (PI: Paul Anderson, CAEE)
Although arsenic contamination of water resources is a global problem, there are still no rapid, reliable, low-cost sensors for on-site detection of low concentrations of arsenic in water. This study combines molecular dynamics modeling with computational fluid dynamics to examine how a metal-organic framework (MOF) compound could be used to develop such a sensor. Design criteria for the sensor include low-cost, stability, durability, selectivity, and the ability to function over a range of pH values.