Modeling On-Site Combined Heat and Power Systems Coupled to Main Process Operation

Abstract

Many production processes work with on-site Combined Heat and Power (CHP) systems to reduce their operational cost and improve their incomes by selling electricity to the external grid. Optimal management of these plants is key in order to take full advantage of the possibilities offered by the different electricity purchase or selling options. Traditionally, this problem is not considered for small cogeneration systems whose electricity generation cannot be decided independently from the main process production rate. In this work, a non-linear gray-box model is proposed in order to deal with this dynamic optimization problem in a simulated sugar factory. The validation shows that with only 52 equations, the whole system behavior is represented correctly and, due to its structure and small size, it can be adapted to any other production process working along a CHP with the same plant configuration.

Optimisation of the Operation of an Industrial Power Plant under Steam Demand Uncertainty

“…They formulated the optimisation as a Mixed-Integer Linear Program (MILP) and implemented it to optimise the operation considering time sensitive electricity prices. Pablos et al [12] presented a non-linear gray-box model of a CHP plant that is coupled to a sugar factory where the production rates of the whole system are optimised from an economic perspective, taking dynamic electricity prices into consideration. Bindlish [13] used a non-linear model for the scheduling of a CHP plant in a chemical site and used linear Model Predictive Control (MPC) to implement the optimised operation.…”

Special Issue on “Process Modelling and Simulation”

“…Our objective was to provide interested readers with an overview of the current state of research, tools and applications on the use of models for simulation and decision support in the process industry. The special issue brings together fourteen contributions on topics ranging from the process systems [1-3] and (bio)chemical engineering [4,5] fields, to software development [6] and applications in heat and power systems [7,8]. Moreover, the hot topic of data mining and machine learning is also discussed from a process engineering perspective in [9,10].…”

Simulation and optimization methods as decision support tools for operation of oil refinery hydrogen networks.

“…Nonetheless, most examples would use simulation as a means to support rigorous mathematical modeling (Chen et al, 2015;Halim and Srinivasan, 2011;Yang and Feng, 2019) and forecasting complex processes (Galan et al, 2019a;Pablos et al, 2019;Suvarna et al, 2019). Most likely these features will be combined with one or more optimization techniques (Biegler et al, 2002), which in the end help making decisions towards process operation and design.…”