Mohammad S. Masnadi

Co-gasification of biosolids with biomass: Thermogravimetric analysis and pilot scale study in a bubbling fluidized bed reactor

This work studied the feasibility of co-gasification of biosolids with biomass as a means of disposal with energy recovery. The kinetics study at 800°C showed that biomass, such as switchgrass, could catalyze the reactions because switchgrass ash contained a high proportion of potassium, an excellent catalyst for gasification. However, biosolids could also inhibit gasification due to interaction between biomass alkali/alkaline earth metals and biosolids clay minerals. In the pilot scale experiments, increasing the proportion of biosolids in the feedstock affected gasification performance negatively. Syngas yield and char conversion decreased from 1.38 to 0.47m(3)/kg and 82-36% respectively as the biosolids proportion in the fuel increased from 0% to 100%. Over the same range, the tar content increased from 10.3 to 200g/m(3), while the ammonia concentration increased from 1660 to 19,200ppmv. No more than 25% biosolids in the fuel feed is recommended to maintain a reasonable gasification.

CO2 enhanced oil recovery: a catalyst for gigatonne-scale carbon capture and storage deployment?

Using carbon dioxide for enhanced oil recovery (CO2-EOR) has been widely cited as a potential catalyst for gigatonne-scale carbon capture and storage (CCS) deployment. Carbon dioxide enhanced oil recovery could provide revenues for CO2 capture projects in the absence of strong carbon taxes, providing a means for technological learning and economies of scale to reduce the cost of CCS. We develop an open-source techno-economic Model of Iterative Investment in CCS with CO2-EOR (MIICE), using dynamic technology deployment modeling to assess the impact of CO2-EOR on the deployment of CCS. Synthetic sets of potential CCS with EOR projects are created with typical field characteristics and dynamic oil and CO2 production profiles. Investment decisions are made iteratively over a 35 year simulation period, and long-term changes to technology cost and revenues are tracked. Installed capacity at 2050 is used as an indicator, with 1 gigatonne per year of CO2 capture used as a benchmark for successful large-scale CCS deployment. Results show that current CO2 tax and oil price conditions do not incentivize gigatonne-scale investment in CCS. For current oil prices (

Energetic productivity dynamics of global super-giant oilfields

45 per bbl–

Global carbon intensity of crude oil production

55 per bbl), the final CO2 tax must reach

Fuel characterization and co-pyrolysis kinetics of biomass and fossil fuels

70 per tCO2 for gigatonne-scale deployment. If oil price alone is expected to induce CCS deployment and learning, oil prices above

Co-gasification of Biomass and Non-biomass Feedstocks: Synergistic and Inhibition Effects of Switchgrass Mixed with Sub-bituminous Coal and Fluid Coke During CO2 Gasification

85 per bbl are required to promote the development of a gigatonne-scale CCS industry. Nonlinear feedbacks between early deployment and learning result in large changes in final state due to small changes in initial conditions. We investigate the future of CCS in five potential ‘states of the world’: an optimistic ‘Base Case’ with a low CO2 tax and low oil price, a ‘Climate Action’ world with high CO2 tax, a ‘High Oil’ world with high oil prices, a ‘Depleting Resources’ world with an increasing deficit in oil supply, and a ‘Forward Learning’ world where mechanisms are in place to drive down the cost of CCS at rates similar to other clean energy technologies. Through multidimensional sensitivity analysis we outline combinations of conditions that result in gigatonne-scale CCS. This study provides insight levels of taxes, learning rates, and oil prices required for successful scale-up of the CCS industry.

From fossil fuels towards renewables: Inhibitory and catalytic effects on carbon thermochemical conversion during co-gasification of biomass with fossil fuels

In addition to supplying fuels to society, the oil industry is a globally-significant consumer of energy. Oil sector energetic productivity – measured as output energy per unit of energy consumed – is a major driver of both industry economics and environmental impacts. Increasing climate change concerns necessitate detailed analysis of the energetic productivity of the oil sector. Using an engineering-based life cycle assessment (LCA) approach, we analyze decades-long historical trends of upstream oil production energy return on investment (EROI) for twenty-five globally significant oil fields (>1 billion barrels recoverable). The net energy ratio (NER) and external energy ratio (EER) are used as two measures of oil field energetic productivity. We find that as depletion causes declines in oil output, the associated energy returns decline significantly, with some fields seeing NER declines exceeding 90%. This decrease is caused by reservoir exhaustion forcing increased energy costs in recovery and processing. Probabilistic simulation allows us to generalize from our dataset: over 25 years, this model predicts ∼40% and ∼20% declines in NER and EER medians, respectively. We also derive a general relationship for projecting evolving NER and EER trends. These results have implications for long-run climate/energy system modeling due to potential large increases in extraction energy as global oilfields age. These effects may result in significant underestimation of future energy demand in the global oil sector in long-run integrated assessment models. Lastly, we perform sensitivity analysis examining implications of model assumptions about electricity provision, flare management, and transport.

Mineral matter interactions during co-pyrolysis of coal and biomass and their impact on intrinsic char co-gasification reactivity

New data enable targeted policy to lessen GHG emissions Producing, transporting, and refining crude oil into fuels such as gasoline and diesel accounts for ∼15 to 40% of the “well-to-wheels” life-cycle greenhouse gas (GHG) emissions of transport fuels (1). Reducing emissions from petroleum production is of particular importance, as current transport fleets are almost entirely dependent on liquid petroleum products, and many uses of petroleum have limited prospects for near-term substitution (e.g., air travel). Better understanding of crude oil GHG emissions can help to quantify the benefits of alternative fuels and identify the most cost-effective opportunities for oil-sector emissions reductions (2). Yet, while regulations are beginning to address petroleum sector GHG emissions (3–5), and private investors are beginning to consider climate-related risk in oil investments (6), such efforts have generally struggled with methodological and data challenges. First, no single method exists for measuring the carbon intensity (CI) of oils. Second, there is a lack of comprehensive geographically rich datasets that would allow evaluation and monitoring of life-cycle emissions from oils. We have previously worked to address the first challenge by developing open-source oil-sector CI modeling tools [OPGEE (7, 8), supplementary materials (SM) 1.1]. Here, we address the second challenge by using these tools to model well-to-refinery CI of all major active oil fields globally—and to identify major drivers of these emissions.