Nearly a century ago, Fischer and Tropsch discovered a means of synthesizing organic compounds ranging from C1 to C70 by reacting carbon monoxide and hydrogen on a catalyst. Fischer–Tropsch synthesis (FTS) is now known as a pseudo-polymerization process taking a mixture of CO as H2 (also known as syngas) to produce a vast array of hydrocarbons, along with various small amounts of oxygenated materials. Despite the decades spent studying this process, it is still considered a black-box reaction with a mechanism that is still under debate. This investigation sought to improve our understanding by taking data from a series of experimental Fischer–Tropsch synthesis runs to build a computational model. The experimental runs were completed in an isothermal continuous stirred-tank reactor, allowing for comparison across a series of completed catalyst tests. Similar catalytic recipes were chosen so that conditional comparisons of pressure, temperature, SV, and CO/H2 could be made. Further, results from the output of the reactor that included the deviations in product selectivity, especially that of methane and CO2, were considered. Cobalt was chosen for these exams for its industrial relevance and respectfully clean process as it does not intrinsically undergo the water–gas shift (WGS). The primary focus of this manuscript was to compare runs using cobalt-based catalysts that varied in two oxide catalyst supports. The results were obtained by creating two differential equations, one for H2 and one for CO, in terms of products or groups of products. These were analyzed using sensitivity analysis (SA) to determine the products or groups that impact the model the most. The results revealed a significant difference in sensitivity between the two catalyst–support combinations. When the model equations for H2 and CO were split, the results indicated that the CO equation was significantly more sensitive to CO2 production than the H2 equation.

Energy harvesting is a promising solution to power billions of ultra-low-power Internet-of-Things devices to enable ubiquitous computing. However, energy harvesters typically output tiny amounts of energy and, therefore, cannot continuously power devices; this leads to intermittent computing, where the energy harvester periodically charges a capacitor to sufficient voltage to power brief computation, until the capacitor's charge is drained, and the cycle repeats. To retain program state across frequent power failures, prior work proposes checkpointing program state to Non-Volatile Memory (NVM) before a power failure. Unfortunately, the most widely deployed, highest performance, and lowest cost devices employ Flash as their NVM, but the power, time, and endurance limitations of Flash writes are incompatible with the frequent NVM checkpoints of intermittent computation.The multi-year data retention of Flash is overkill for retaining program state across intermittent computing's short power-off times (e.g., <1s). We observe that even after computation stops due to low voltage, charge remains in the system; this remaining charge keeps the voltage high enough to maintain data in SRAM-effectively making it a NVM-for 10's of minutes post-power loss. This paper explores how to leverage SRAM's data remanence to boost common-case performance and energy efficiency for Flash-based intermittent computation systems. We propose TotalRecall, a library-level, in-situ, checkpointing technique that retains program state in SRAM and identifies when SRAM acts as a NVM, falling back to conventional NVM checkpoints in the rare event of long off times. Our evaluation, on real hardware, using benchmarks from Texas Instruments, shows that TotalRecall incurs overheads as low as 0.8%-up to over 350x faster than checkpointing to Flash.

No abstract

No abstract

No abstract