The development of a bio-based economy has its foundations in the development of efficient processes to optimize biomass potential. In this context there are a multitude of molecules that can be either synthetized or recovered from biomass, among those the so-called 12 building-blocks reported by the US Department of Energy. Even if their identification and importance is clearly defined, research efforts concerning the purification or separation of these platform molecules are limited. To fill this gap, different configurations for the purification of levulinic acid recovered from lignocellulosic biomass are examined and compared in this work. In particular, hybrid configurations obtained by the combination of liquid-liquid extraction and distillation have been considered. It was demonstrated how a deep understanding of the subspace including all extraction-assisted simple column distillation configurations represents a fundamental step in the synthesis of different process alternative... [more]

Process Intensification (PI) is a vast and growing area in Chemical Engineering, which deals with the enhancement of current technology to enable improved efficiency; energy, cost, and environmental impact reduction; small size; and better integration with the other equipment. Since process intensification results in novel, but complex, systems, it is necessary to rely on optimization and control techniques that can cope with such new processes. Therefore, this review presents some advancements in the field of process intensification that are worthy of exploring in detail in the coming years. At the end, several important open questions that can be taken into consideration in the coming years are listed.

Advice and best practices for teaching conceptual process flowsheeting, simulation, and design at the third year undergraduate level. Discusses setting course goals, integration with the rest of the curriculum, and delivery techniques. Practical strategies for tutorials, exams, lectures, and projects. Training TAs for experiential learning workshops. Best practices in teaching distillation design. This is the Award Lecture for AIChE's David Himmelblau Award for Innovations in Computer-Based Chemical Engineering Education. Live lecture given via APMonitor.com as a part of the AIChE's Computing and Systems Technology division webinar series.

Due to the expanding concern on cleaner production and sustainable development aspects, a technology shift is needed for the hydrogen production, which is commonly derived from natural gas. This work aims to synthesise a large-scale bio-hydrogen network in which its feedstock, i.e., bio-methane, is originated from landfill gas and palm oil mill effluent (POME). Landfill gas goes through a biogas upgrader where high-purity bio-methane is produced, while POME is converted to bio-methane using anaerobic digestor (AD). The generated bio-methane is then distributed to the corresponding hydrogen sink (e.g., oil refinery) through pipelines, and subsequently converted into hydrogen via steam methane reforming (SMR) process. In this work, P-graph framework is used to determine a supply network with minimum cost, while ensuring the hydrogen demands are satisfied. Two case studies in the West and East Coasts of Peninsular Malaysia are used to illustrate the feasibility of the proposed model. In C... [more]

The attainable region (AR) is a region in a performance space in which all physically realizable reactor network designs must exist. ARs have been used since the 1960s for solving reactor network synthesis problems. The benefits of these methods are that the feasibility of a performance target can be assessed prior to running a synthesis routine, the solutions they give are guaranteed to be physically realizable, and a design can be made robust to uncertainties in feed and performance targets by assessing whether a solution and the range of its possible values lie within the AR, just to name a few. In this article, the theory of attainable regions is extended to bring these benefits to the heat exchanger network (HEN) synthesis problem. Basic properties of the HEN-AR are proven and a synthesis method using the AR is presented with examples.

In this talk, I give an overview of the chemical engineering curriculum at McMaster University as it relates to the 1.5 year process design sequence. The courses outside the design sequence were recently restructured and redesigned to create an environment with more modelling and algorithmic thinking/algorithmic problem solving. This includes a statistics course and a big data / machine learning course. The end result is that the design sequence is able to focus on state of the art tools and methods for process design because students receive many fundamental principles before the design sequence begins.

Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.