Integrative Investment Appraisal of a Lignocellulosic Biomass-to-Ethanol Industry

While theoretically more efficient than starch-based ethanol production systems, conversion of lignocellulosic biomass to ethanol is not without major challenges. A multi-region, multi-period, mixed integer mathematical programming model encompassing alternative feedstocks, feedstock production, delivery, and processing is developed. The model is used to identify key cost components and potential bottlenecks, and to reveal opportunities for reducing costs and prioritizing research. The research objective was to determine for specific regions in Oklahoma the most economical source of lignocellulosic biomass, timing of harvest and storage, inventory management, biorefinery size, and biorefinery location, as well as the breakeven price of ethanol, for a gasification-fermentation process. Given base assumptions, gasification-fermentation of lignocellulosic biomass to ethanol may be more economical than fermentation of corn grain. However, relative to conventional fermentation processes, gasification-fermentation technology is in its infancy. It remains to be seen if the technology will be technically feasible on a commercial scale.biomass, biorefinery location, ethanol, integrative investment appraisal, logistics, mixed integer programming, Resource /Energy Economics and Policy,

Negative bases and automata

We study expansions in non-integer negative base -{\beta} introduced by Ito and Sadahiro. Using countable automata associated with (-{\beta})-expansions, we characterize the case where the (-{\beta})-shift is a system of finite type. We prove that, if {\beta} is a Pisot number, then the (-{\beta})-shift is a sofic system. In that case, addition (and more generally normalization on any alphabet) is realizable by a finite transducer. We then give an on-line algorithm for the conversion from positive base {\beta} to negative base -{\beta}. When {\beta} is a Pisot number, the conversion can be realized by a finite on-line transducer

Sticky Seeding in Discrete-Time Reversible-Threshold Networks

When nodes can repeatedly update their behavior (as in agent-based models from computational social science or repeated-game play settings) the problem of optimal network seeding becomes very complex. For a popular spreading-phenomena model of binary-behavior updating based on thresholds of adoption among neighbors, we consider several planning problems in the design of \textit{Sticky Interventions}: when adoption decisions are reversible, the planner aims to find a Seed Set where temporary intervention leads to long-term behavior change. We prove that completely converting a network at minimum cost is $\Omega(\ln (OPT) )$-hard to approximate and that maximizing conversion subject to a budget is $(1-\frac{1}{e})$-hard to approximate. Optimization heuristics which rely on many objective function evaluations may still be practical, particularly in relatively-sparse networks: we prove that the long-term impact of a Seed Set can be evaluated in $O(|E|^2)$ operations. For a more descriptive model variant in which some neighbors may be more influential than others, we show that under integer edge weights from $\{0,1,2,...,k\}$ objective function evaluation requires only $O(k|E|^2)$ operations. These operation bounds are based on improvements we give for bounds on time-steps-to-convergence under discrete-time reversible-threshold updates in networks.Comment: 19 pages, 2 figure

Analysis of fractional order systems using newton iteration-based approximation technique

Fractional differential equations play a major role in expressing mathematically the real-world problems as they help attain good fit to the experimental data. It is also known that fractional order controllers are more flexible than integer order controllers. But when it comes to the numerical approximation of fractional order functions inaccuracies arise if the conversion technique is not chosen properly. So, when a fractional order plant model is approximated to an integer order system, it is required that the approximated model be accurate, as the overall system performance is based on the estimated integer order model. Nitisha-Pragya-Carlson (NPC) is a recent approximation technique proposed in 2018 to derive the rational approximation of fractional order differ-integrators. In this paper, three fractional order plant models having fractional powers 3.1, 1.25 and 1.3 is analyzed in frequency domain in terms of magnitude and phase response. The performance of approximated third and second order NPC based integer model is studied and compared with the integer models developed using other existing technique. The approximation error is calculated by comparing the frequency response of the developed models with the ideal response. It has been found that in all the three examples NPC based models are very much close to the ideal values. Hence proving the efficacy of NPC technique in approximation of fractional order systems

Radix Conversion for IEEE754-2008 Mixed Radix Floating-Point Arithmetic

Conversion between binary and decimal floating-point representations is ubiquitous. Floating-point radix conversion means converting both the exponent and the mantissa. We develop an atomic operation for FP radix conversion with simple straight-line algorithm, suitable for hardware design. Exponent conversion is performed with a small multiplication and a lookup table. It yields the correct result without error. Mantissa conversion uses a few multiplications and a small lookup table that is shared amongst all types of conversions. The accuracy changes by adjusting the computing precision