RESUMO
Shale gas is revolutionizing the U.S. energy and chemical commodity landscape and can ease the transition to a sustainable decarbonized economy. This work develops an equation-oriented (EO) multiscale modeling framework using the open-source IDAES-PSE platform that tractably incorporates microkinetic detail in process design via reduced-order kinetic (ROK) models. Using multiobjective optimization with embedded heat integration and life-cycle analysis, we simultaneously minimize the minimum selling price of liquid hydrocarbons (e.g., liquid fuels/additives from shale gas) and process emissions (via a CO2 tax). Optimization reduces greenhouse gas emissions per MJ of fuel produced by over 35% compared to the literature and achieves a carbon efficiency of 87%. The optimizer changes the recycling rate, temperatures, and pressures to mitigate the effect of ROK model-form uncertainty on product portfolio predictions. Moreover, we show that the optimal process design is insensitive to changing CO2 tax rates. Finally, the EO framework enables a fast sensitivity analysis of shale gas composition variability across 12 regions of the Eagle Ford basin. These results highlight the benefits of the open-source EO framework: fast, scalable, customized, and reproducible system analysis and optimization for sustainable energy technologies beyond shale utilization.
RESUMO
Strategies targeting methane (CH4) and nitrous oxide (N2O) emissions are critical to meeting global climate targets. Existing literature estimates the emissions of these gases from specific sectors, but this knowledge must be synthesized to prioritize and incentivize CH4 and N2O mitigation. Accordingly, we review emissions sources and mitigation strategies in all key sectors (fuel extraction and combustion, landfilling, agriculture, wastewater treatment, and chemical industry) and the role of carbon markets in reducing emissions. The most accessible reduction opportunities are in the hydrocarbon extraction and waste sectors, where half (>3 Gt-CO2e/year) of the emissions in these sectors could be mitigated at no net cost. In total, 60% of CH4 emissions can be mitigated at less than $50/t-CO2. Expanding the scope of carbon markets to include these emissions could provide cost-effective decarbonization through 2050. We provide recommendations for carbon markets to improve emissions reductions and set prices to appropriately incentivize mitigation.
RESUMO
A framework to obtain optimal operating conditions is proposed for a cryogenic air separation unit case study. The optimization problem is formulated considering three objective functions, 11 decision variables, and two constraint setups. Different optimization algorithms simultaneously evaluate the conflicting objective functions: the annualized cash flow, the efficiency at the compression stage, and capital expenditures. The framework follows a modular approach, in which the process simulator PRO/II and a Python environment are combined. The results permit us to assess the applicability of the tested algorithms and to determine optimal operational windows based on the resultant 3-D Pareto fronts.
RESUMO
Air separation systems are crucial in the production of oxygen, which has gained particular relevance during the COVID-19 outbreak. Mechanical ventilation can compensate respiratory deficiencies along with the use of medical oxygen in vulnerable patients infected with this disease. In this contribution, a many-objective simulation-based optimization framework is proposed for determining eleven decision variables for the operation of an air separation unit. The framework combines the capabilities of the process simulator PRO/II with a Python environment. Three objective functions are optimized together towards the construction of a 3-D Pareto front. Results provide insightful information regarding the most adequate operating conditions of the unit, including the definition of an operational window rather than a single operational point.
