For the first time have boron-containing weak Lewis acids been demonstrated to be active components of Frustrated Lewis Pair (FLP) catalysts in the hydrogenation of ketones to alcohols. Combining the organosuperbase (pyrr) P=NtBu with the Lewis acid 9-(4-CF -C H )-BBN generated an "inverse" FLP catalyst capable of hydrogenating a range of aliphatic and aromatic ketones including N-, O- and S-functionalized substrates and bio-mass derived ethyl levulinate. Initial computational and experimental studies indicate the mechanism of catalytic hydrogenation with "inverse" FLPs to be different from conventional FLP catalysts that contain strong Lewis acids such as B(C F ) .

A series of mono- and binuclear Lewis acid-base complexes of the formulas N[CHCHN(Pr)]P→LA [LA = BH (8), Ga(CF) (10), GaCl (11)], LA←N[CHCHN(Pr)]P [LA = Al(CF) (6a), AlMe (6b), AlEt (6c), AlBu (6d), BF (13)], and LA←N[CHCHN(Pr)]P→LA [Lewis acid (LA) = Al(CF) (7a), AlMe (7b), AlEt (7c), AlBu (7d), AlCl (7e), BH (9)] were generated from reactions of Verkade's base, N[CHCHN(Pr)]P (1), with various boron-, aluminum-, and gallium-containing Lewis acids, and characterized by multinuclear NMR spectroscopy. {N[CHCHN(Pr)]P→CH}[BF] (5) was synthesized via the treatment of 1 with [CH][BF]. The reaction of 1 with B(CF), followed by the addition of [PhC][BCl], gave rise to the rearranged borate salt [PNCH(Pr)][BCl] (3), while treating 1 with [PhC][BCl] exclusively afforded {N[CHCHN(Pr)]PH}[BCl] (4). Reactions of 1 with 2 equiv of GaCl and BF, respectively, afforded the novel phosphenium gallate and borate salts 12a, 12b, and 15. The solid-state structures of 1, 3-5, 6b, 7a, 7b, 7e, 8, 10, 11, 12b, 13, and 15 were determined by X-ray crystallography.

The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

Recent trends in digitization and automation of information systems have led to the Industry 4.0 revolution in manufacturing systems. With the emergence of integrated “smart” systems that communicate through the cloud, collecting and manipulating the system data became a key yet, challenging component for developing optimal control strategies for these complex systems. In this work, we propose a strategy to address this problem with the case study on an air separation unit (ASU). Our approach involves developing an ASU's controllers via high‐fidelity modeling, studies in data‐driven reduced‐order models, and providing implementable control policies for the high‐fidelity model. Connecting the high‐fidelity model to a smart manufacturing platform allows integration into other smart manufacturing tools and applications. Since the high‐fidelity model is computationally challenging for online optimization tasks, such as model predictive control, surrogate models are generated that represent the high‐fidelity model's behavior. The derived reduced‐order models are then embedded into a model predictive control formulation for the optimal control of the whole process through multiparametric programming. A multiparametric approach based on solving a small portion of the multiparametric program is proposed to reduce the computational overhead. We then close the loop by deploying the developed controllers on the high‐fidelity model for tuning with prospects of employing them on the real industrial plant.

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