Dynamic process intensification

Advanced Modeling and Optimization Strategies for Process Synthesis

This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation-based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning-and quantum computing-assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies.

Rapid Multi-Objective Optimization of Periodically Operated Processes Based on the Computer-Aided Nonlinear Frequency Response Method

The dynamic optimization of promising forced periodic processes has always been limited by time-consuming and expensive numerical calculations. The Nonlinear Frequency Response (NFR) method removes these limitations by providing excellent estimates of any process performance criteria of interest. Recently, the NFR method evolved to the computer-aided NFR method (cNFR) through a user-friendly software application for the automatic derivation of the functions necessary to estimate process improvement. By combining the cNFR method with standard multi-objective optimization (MOO) techniques, we developed a unique cNFR–MOO methodology for the optimization of periodic operations in the frequency domain. Since the objective functions are defined with entirely algebraic expressions, the dynamic optimization of forced periodic operations is extraordinarily fast. All optimization parameters, i.e., the steady-state point and the forcing parameters (frequency, amplitudes, and phase difference), are determined rapidly in one step. This gives the ability to find an optimal periodic operation around a sub-optimal steady-state point. The cNFR–MOO methodology was applied to two examples and is shown as an efficient and powerful tool for finding the best forced periodic operation. In both examples, the cNFR–MOO methodology gave conditions that could greatly enhance a process that is normally operated in a steady state.

Process Control and Energy Efficiency

We review the impact of control systems and strategies on the energy efficiency of chemical processes. We show that, in many ways, good control performance is a necessary but not sufficient condition for energy efficiency. The direct effect of process control on energy efficiency is manyfold: Reducing output variability allows for operating chemical plants closer to their limits, where the energy/economic optima typically lie. Further, good control enables novel, transient operating strategies, such as conversion smoothing and demand response. Indirectly, control systems are key to the implementation and operation of more energy-efficient plant designs, as dictated by the process integration and intensification paradigms. These conclusions are supported with references to numerous examples from the literature.

Estudo termoquímico assistido por computador para a produção de biodiesel

A importância de melhorar os processos de transformação industrial para processos mais eficientes é parte dos desafios atuais. Específicamente, o desenvolvimento de processos mais eficientes na produção de biocombustíveis, onde os processos de reação e separação podem ser intensificados, é de grande interesse para reduzir o consumo de energia associado ao processo. No caso do Biodiesel, o processo é definido por uma reação química e pelos componentes associados ao processo, onde o estudo termoquímico busca desenvolver cálculos para o entendimento subsequente do processo de reação e purificação. Assim, a análise da mistura dos componentes usando o simulador de processo Aspen Plus V9® descreve o estudo termoquímico. O método termodinâmico UNIFAC-DMD foi utilizado para estimar os parâmetros binários de equilíbrio dos reagentes usando o simulador. Os aspectos analisados apresentam o comportamento dos componentes em diferentes condições de temperatura, o comportamento azeotrópico e as condições termoquímicas determinadas