Combined Heat and Power
Improving Sports Media's Crystal Ball for National Basketball Association Playoff Elimination
The National Basketball Association (NBA) is divided into two conferences, each of which is comprised of fifteen teams. At the end of the regular season, the top eight teams from each conference, based on winning percentage, compete in the playoffs. An integer-programming model determines when a team has guaranteed its position in the playoffs, or, conversely, when it has been eliminated before the completion of the regular season. At the end of the regular season, there are instances in which teams’ winning percentages are tied. Ties are broken using seven independent criteria, and we implement these by determining: (i) when a team has been eliminated from the playoffs; and (ii) how many games a team must win in order to clinch a playoff position. The results are published on the RIOT website so fans can follow their favorite teams’ playoff standings. We compare the time at which (and day on which) these results are published as the NBA official standings; in many cases, RIOT notifies
the public prior to the NBA by, on average, 4.1 games. We also describe a scenario in which the NBA erroneously reported the Boston Celtics had clinched a playoff spot and a show that the Golden State Warriors had clinched a playoff spot before the official announcement by the NBA.
We compare our results against what is posted on the NBA website for the 2017-2018 regular season. The figure shows the date on which the respective information source determined when a given team either clinched first place in the conference, clinched a playoff position, or was eliminated from the playoffs for the Eastern Conference. Cells highlighted in green show RIOT
outperforming the NBA’s published results and blue represents the case in which the two tie, where we define outperformance as the ability of a model to determine earlier that a given team had either clinched or been eliminated. https://s2.smu.edu/~olinick/riot/detail_nba_numbers.html
Concentrating Solar Power Mirror-Washing Optimization
Solid-Oxide Fuel Cell Assembly for Unconventional Oil and Gas Production
We study a multi-objective design and dispatch optimization model of a solid-oxide fuel cell assembly for unconventional oil and gas production. Fuel cells are galvanic cells which chemically convert hydrocarbon-based fuels to electricity. The Geothermic Fuel Cell concept involves utilizing heat from fuel cells during electricity generation to provide thermal energy required to pyrolyze kerogen into a mixture of oil, hydrocarbon gas and carbon-rich shale coke.
Heat Limitations in Underground Production Scheduling
A problem in the mining industry is production scheduling, or determining when, if ever, notional three-dimensional blocks of ore should be mined. Often lacking in underground production scheduling models are heat limitations, driven largely by the equipment used for underground activities such as development, extraction, and back-filling. To correct this, we attribute a specific heat load to each mining activity owing to equipment use, auto compression, broken rock and strata rock. In addition, accurate engine modeling is necessary for appropriate heat assignment. We incorporate heat into a knapsack constraint in an integer programming model that produces more realistic schedules; adhering to them could increase revenue by lowering refrigeration costs for the mine.
Oluwaseun Ogunmodede (Source: Chasm Consulting, 2015)
Design and Dispatch of Concentrating Solar Power Tower Systems with Utility-scale Photovoltaics
Due to technology immaturity, the current generation of commercial-scale concentrated solar power (CSP) with thermal energy storage (TES) systems operate under relaxed requirements. As renewable energy penetration grows, so will the responsibility of CSP systems to be highly flexible sources of generation. To meet this challenge, system designs will have to account for various current and future market conditions. Designing CSP systems with and without photovoltaic (PV) generation requires accurate, but computationally inexpensive, system modeling. To optimize the design of a CSP system requires dispatch optimization to evaluate a potential design under various resource and market constraints.
Scheduling Optimization for Continuous Steel Casting and Rolling Operations
In continuous steel casting operations, molten steel is batched into heats inside a ladle that is cast into slabs, as seen in the figure. The slabs are then rolled into coils, the final product. We present a mixed integer program that is solved using state-of-the-art software to produce a daily casting schedule. This model minimizes penalties incurred by violating plant best practices while strictly adhering to safety and logical constraints to manage risk for manufacturing incidents that can occur in the rolling mill. A heuristic is also used to produce an initial feasible solution in order to expedite the generation of near-optimal schedules.
Mathematical Model of Hybrid Power System
A mathematical model designs and operates a hybrid power system consisting of diesel generators, photovoltaic cells and battery storage to minimize fuel use at remote sites subject to meeting variable demand profiles, given the following constraints: power generated must meet demand in every time period; power generated by any technology cannot exceed its maximum rating; and best practices should be enforced to prolong the life of the technologies. We solve this optimization model in two phases: (i) we obtain the design and dispatch strategy for an hourly load profile, and (ii) we use the design strategy, derived in (i), as input to produce the optimal dispatch strategy at the minute-level. Our contributions consist of: combining a year-long hourly optimization procurement strategy with a minute-level dispatch strategy, and using a high-fidelity battery model at the minute level derived from electrochemical engineering principles that incorporate temperature and voltage transient effects. We solve both phases of the optimization problem to within 5% of optimality and demonstrate that solutions from the minute-level model more closely match the load, more closely capture battery and generator behavior, and provide fuel savings from a few percent to 30% over that provided by the hour-level model for the tested scenarios.
Underground Mine Design and Scheduling
Large underground hard rock mines are complex industrial projects whose economic success depends on intelligent design and detailed scheduling. The choice of ore extraction method influences decisions regarding the type of infrastructure and sequence of activities for an underground mining operation. Current industry practice selects a single ore extraction method based on ore body characteristics (e.g., grade, depth, and rock hardness), and, as a function of this method, produces an extraction schedule. Considering multiple methods requires navigating geotechnical complexities associated with the interaction of different mining methods; a feasible schedule must consider both intra- and inter-mining-method precedence. We are researching a methodology that combined formal optimization with heuristics to design an underground mine consisting of two extraction methods in order to maximize profitability via its corresponding production schedules over the life of the mine. A heuristic determines by which method an area of the mine is extracted and an optimization model determines the time at which each activity takes place. We empirically demonstrate that the optimization-based heuristic generates solutions that improve profitability.
Designing River Basin Storage Along the Lower South Platte
As the demand for water within the South Platte Basin grows, we seek to mitigate the shortage that will ensue by the optimal placement of additional reservoir storage while including water transfers via pipeline.