This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark dataset which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the prealigned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.

Optimization problems often have a subset of parameters whose values are not known exactly or have yet to be realized. Nominal solutions to models under uncertainty can be infeasible or yield overly optimistic objective function values given the actual parameter realizations. Worst-case robust optimization guarantees feasibility but yields overly conservative objective function values. The use of probabilistic guarantees greatly improves the performance of robust counterpart optimization. We present new a priori and a posteriori probabilistic bounds which improve upon existing methods applied to models with uncertain parameters whose possible realizations are bounded and subject to unspecified probability distributions. We also provide new a priori and a posteriori bounds which, for the first time, permit robust counterpart optimization of models with parameters whose means are only known to lie within some range of values. The utility of the bounds is demonstrated through computational case studies involving a mixed-integer linear optimization problem and a linear multiperiod planning problem. These bounds reduce the conservatism, improve the performance, and augment the applicability of robust counterpart optimization.

The pursuit toward an environmentally sustainable energy landscape requires the development of economically competitive renewable processes. Efficient utilization of renewable resources is an important first step toward meeting this goal. To this extent, we introduce a systematic deterministic global optimization-based process synthesis framework that determines the most profitable processes to produce benzene, toluene, and/or xylenes from biomass via methanol. Our framework incorporates several novel, competing, and/or commercial technologies. We quantify the effect that biomass type has on the overall profit of a refinery by investigating forest residues, agricultural residues, and perennial crops as potential feedstocks. A thorough economic analysis, together with material, energy, carbon, and greenhouse gas balances, are provided for every proposed process design. The capability of our proposed approach is illustrated through several case studies that produce varying ratios of p-, o-, and m-xylene across several refinery scales. The most profitable aromatics refineries consistently produce p-xylene, while o-xylene refineries consistently have the lowest required investment costs. The net present values for the biomass to aromatics, BTA, refineries producing 2000 t per day of product are as high as $1200 MM dollars with payback periods less than 10 years.