Name: João Gustavo Coelho Pena
Type: PhD thesis
Publication date: 17/07/2019

Namesort ascending Role
Jose Leandro Félix Salles Advisor *
Helder Roberto de Oliveira Rocha Advisor *

Examining board:

Namesort ascending Role
Rodrigo de Alvarenga Rosa External Examiner *
Marcelo Eduardo Vieira Segatto Internal Examiner *
Jussara Farias Fardin Internal Examiner *
Helder Roberto de Oliveira Rocha Advisor *
Edilson Fernandes de Arruda External Examiner *

Summary: Manufacturing iron-and steel is one of the most energy intensive and pollutant industrial activities. On the other hand, the majority of these pollutant gases can be used as fuel for cogeneration of electricity and process steam; in that case, then the efficient utilisation of these gases is significant for energy saving and CO2 reduction. However, the management of this system is a complex activity, mainly because of the imbalances
between the production and consumption profiles of the gases, the capacity limitations of the gas accumulators and their operational restrictions, as well as the restrictions for the use of energy in the thermoelectric power plants. As a result, when a temporary excess of byproduct gases occurs over a timescale, the byproduct gasholder exceeds capacity, and this leads to byproduct gas flaring, which indicates an economic loss and pollution
of the environment. However, a shortage of byproduct gas causes mechanical trouble to the byproduct gasholder and affects the production process. Thus, it is of great importance to optimise the scheduling and distribution of byproduct gases to reduce byproduct gas flaring or shortage, and to maintain the stability of the byproduct gases distribution system. This thesis addresses the real-time by-product gas scheduling in an integrated ironand steel-making industry with uncertainty in by-product gas flows by means of a rolling horizon algorithm. Adaptive time-series models determined from real data perform forecast for each producer and consumer of by-product gases in main units of the steelmaking plant. The individual consumptions of the blast furnace and coke oven gases are
modelled using the seasonal Holt-Winters method with smoothing constants estimated via genetic algorithm, WHEREas the individual productions of the blast furnace and coke oven are identified from autoregressive and integrated moving-average. LDG gas production is forecasted using a heuristic method that leverages the operational information. The model’s parameters are updated periodically due to the nonlinearities present in the time
series. After the forecasting phase, the algorithm performs short-term decisions using a MILP optimization model, that minimizes the imbalance between the random dynamics of the by-product fuel generation and consumption and maximizes the energy efficiency. Computational simulations suggest that the operational stability of the gas holders and the electrical energy production increase, WHEREas the waste of gases in flare stack decreases,
when the control horizon of the rolling horizon algorithm is reduced. Particle swarm optimization was applied to identify reasonable penalty factors which were used in the MILP model to obtain reasonable optimisation of the byproduct gas system.

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