Carbon capture from natural gas combined cycle power plants: Solvent performance comparison at an industrial scale

Natural gas is an important source of energy. This article addresses the problem of integrating an existing natural gas combined cycle (NGCC) power plant with a carbon capture process using various solvents. The power plant and capture process have mutual interactions in terms of the flue gas flow rate and composition vs. the extracted steam required for solvent regeneration. Therefore, evaluating solvent performance at a single (nominal) operating point is not indicative and solvent performance should be considered subject to the overall process operability and over a wide range of operating conditions. In the present research, a novel optimization framework was developed in which design and operation of the capture process are optimized simultaneously and their interactions with the upstream power plant are fully captured. The developed framework was applied for solvent comparison which demonstrated that GCCmax, a newly developed solvent, features superior performances compared to the monoethanolamine baseline solvent

Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS) for the computational analyses of high speed reacting flows

The principal objective is to extend the boundaries within which large eddy simulations (LES) and direct numerical simulations (DNS) can be applied in computational analyses of high speed reacting flows. A summary of work accomplished during the last six months is presented

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes