Virtual Sector Profiles for Innovation Sharing in Process Industry – Sector 01: Chemicals

Production data in process industry are proprietary to a company since they are key to the process design and technology expertise. However, data confidentiality restrains industry from sharing results and advancing developments in and across process sectors. Using virtual profiles that simulate the typical operating modes of a given process industry offers an elegant solution for a company to share information with the outside world. This paper proposes a generic methodology to create sector blueprints and applies it to the chemicals industry. It details the profile of a typical chemical site based on essential units and realistic data gathered from existing refineries and chemical plants.

Integrating Rate Based Models into a Multi-Objective Process Design & Optimisation Framework using Surrogate Models

Levent Sinan Teske

In the development of energy and chemical processes, the process engineers extensively apply computer aided methods to design & optimise these processes and corresponding process units. Such applications are multi-scale modelling and multi-objective optimisation methods. Multi-objective optimisation of super-structured process designs are expensive in CPU-time due to the high number of potential configurations and operation conditions to be calculated. Thus single process units are generally represented by simple models like equilibrium based (chemical or phase equilibrium) or specific short cut models. In the development of new processes, kinetic effects or mass transport limitations in certain process units may play an important role, especially in multiphase chemical reactors. Therefore, it is desirable to represent such process units by experimentally derived rate based models (i.e. reaction rates and mass transport rates) in the process flowsheet simulators used for the extensive multi-objective optimisation. This increases the trust engineers have in the results and allows enriching the process simulations with newest experimental findings. As most rate based models are iteratively solved, a direct incorporation would cause higher CPU-time that penalises the use of multi-objective optimisation. A global surrogate model (SUMO) of a rate based model was successfully generated to allow its incorporation into a process design & optimisation tool which makes use of an evolutionary multi-objective optimisation. The methodology was applied to a fluidised bed methanation reactor in the process chain from wood to Synthetic Natural Gas (SNG). Two types of surrogate model, an ordinary Kriging interpolation and an artificial neural network, were generated and compared to its underlying rate based model and the chemical equilibrium model. The analysis showed that kinetic limitations have significant influence on the result already for standard bulk gas chemical components. A case study applying the previous version of the process design model and the revised version (with rate based model introduced as a set of five surrogate models) will demonstrate that the prediction uncertainties of the process design & optimisation methodology are reduced due to the integration of the rate based model of the fluidised bed methanation reactor. It will be shown that the different process design models predict considerably different optimal operating conditions of the Wood-to-SNG process. This emphasises the importance of the integration of rate based models into the process design models. The presented approach has been developed for the fluidised bed methanation reactor, however, it is a generic approach which can be applied to other process unit technologies as well. Future investigations will target other technologies to further improve the process design & optimisation predictions and support project development.

EPFL2014

Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage

Manuel Cordova, Benjamin André René Meyer, Boodsarin Sawatlon, Matthew Wodrich

The increasing urgency to make chemical processes more environmentally friendly while continuing to derive the chemicals required for modern society from renewable resources requires the development of a forthcoming generation of synthetic processes and the catalysts needed to facilitate these reactions. Recently, applications of machine-learning (ML) algorithms involving catalysis have begun to appear with increasing frequency, as they constitute an attractive pathway both for discovering prospective species and identifying trends surrounding catalytic behavior, principally because the number of potential catalysts that can be examined greatly exceeds those found in more traditional experimental or theoretical approaches. Here, we harness a data-driven approach powered by ML in tandem with molecular volcano plots to estimate the activity of over 143,000 homogeneous nickel catalysts bearing phosphine and N-heterocyclic carbene ligands for the reductive C(sp(2))-O cleavage reaction in aryl ether compounds, an important step in the degradation of biomass (lignin) into industrially useful feedstock chemicals. Our computational workflow reveals that a vast majority of Ni-phosphine and Ni-carbene catalysts are not ideally tuned to facilitate this reaction. An analysis of those species identified as being the most promising uncovers a clear catalytic design strategy that can be exploited in an experimental setting to enhance the rate of reductive C(sp(2))-O cleavage of aryl ether compounds.

AMER CHEMICAL SOC2020

Integrating Rate Based Models into a Multi-Objective Process Design & Optimisation Framework using Surrogate Models

Levent Sinan Teske

EPFL2014