Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models

Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models

Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models

Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models

Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models

Model-Based Optimization of Cyclic Operation of Acetone-Butanol-Ethanol (ABE) Fermentation Process with ex Situ Butanol Recovery (ESBR) for Continuous Biobutanol Production

This paper proposes a model-based optimization strategy for a fermentation process coupled with an ex situ butanol recovery-by-adsorption (termed “ESBR-by-adsorption” hereafter) process used for continuous biobutanol production. The ESBR-by-adsorption system exhibits cyclic dynamic behavior caused by the periodic switching of the adsorption column for its renewal. Since performance of such a system is largely determined by its dynamic behavior seen after converging to Cyclic Steady State (CSS), the optimization strategy should search for the optimal operating condition leading to the most profitable CSS. For the CSS optimization, we select key optimization variables and define the objective function and constraints. The resulting CSS optimization problem is strongly nonconvex, largely due to the various nonlinearities in the objective function and constraints, e.g., those in the kinetics of the ABE fermentation and adsorption. To alleviate the numerical convergence problem associated with nonconvex optimization problems, we adopt an initialization strategy of identifying a feasible solution region and a “good” initial guess through a coarse grid search. With the initialization strategy, two CSS optimization approaches, “sequential” and “simultaneous,” are examined for the system. With the model and simulation, performances of the two approaches are compared with respect to varying qualities of the initial guess to propose an effective practical CSS optimization strategy for the ESBR-by-adsorption system. The optimized continuous production by the ESBR-by-adsorption system showed significantly improved volumetric productivity of butanol, 5.5- and 3.7-fold increases respectively over the batch fermentation or semibatch fermentation with in situ product recovery

Rapid Dye Adsorption via Surface Modification of TiO₂ Photoanodes for Dye-Sensitized Solar Cells

A facile method for increasing the reaction rate of dye adsorption, which is the most time-consuming step in the production of dye-sensitized solar cells (DSSCs), was developed. Treatment of a TiO₂ photoanode with aqueous nitric acid solution (pH 1) remarkably reduced the reaction time required to anchor a carboxylate anion of the dye onto the TiO₂ nanoparticle surface. After optimization of the reaction conditions, the dye adsorption process became 18 times faster than that of the conventional adsorption method. We studied the influence of the nitric acid treatment on the properties of TiO₂ nanostructures, binding modes of the dye, and adsorption kinetics, and found that the reaction rate improved via the synergistic effects of the following: (1) electrostatic attraction between the positively charged TiO₂ surface and ruthenium anion increases the collision frequency between the adsorbent and the anchoring group of the dye; (2) the weak anchoring affinity of NO₃^– in nitric acid with metal oxides enables the rapid coordination of an anionic dye with the metal oxide; and (3) sufficient acidity of the nitric acid solution effectively increases the positive charge density on the TiO₂ surface without degrading or transforming the TiO₂ nanostructure. These results demonstrate the developed method is effective for reducing the overall fabrication time without sacrificing the performance and long-term stability of DSSCs