Exploitation of multi-objective optimization in retrofit analysis: a case study for the iron and steel production

Abstract Over the past few decades the issues related to the energy consumption and the climate change have been increased and they have achieved a significant position on the sustainability agenda of the steel industry. Steel production is among the largest energy-intensive industrial processes in the world, as well as one of the most important CO 2 emission sources. However, the major role of steel utilisation in the modern society is undeniable. The challenges of industrial energy systems aim at achieving CO 2 minimization, without neglecting energy efficiency as well as the development of effective models and strategies for process optimization. The application of Process Integration (PI) methods to the integrated steelmaking route, aims at achieving a reduction in the CO 2 emission by optimizing material and energy systems. The work presented in this paper is devoted to the development of a model for optimal exploitation of energy resources and by-products in integrated steelworks through application of multi-objective optimisation techniques. Cases of exploitation of the system within the management of the process gases are presented in a retrofit scenario and compared to the case of nominal operation

Cassava processing wastewater as a platform for third generation biodiesel production

ABSTRACT This study aimed to evaluate third generation biodiesel production by microalgae Phormidium autumnale using cassava processing wastewater as a platform. Experiments were performed in a heterotrophic bubble column bioreactor. The study focused on the evaluation of the bioreactor (batch and fed-batch) of different operational modes and the analysis of biofuel quality. Results indicate that fed-batch cultivations improved system performance, elevating biomass and oil productions to 12.0 g L−1 and 1.19 g L−1, respectively. The composition of this oil is predominantly saturated (60 %) and monounsaturated (39 %), resulting in a biodiesel that complys with U.S., European and Brazilian standards. The technological route developed indicates potential for sustainable production of bulk oil and biodiesel, through the minimization of water and chemical demands required to support such a process

Efficient approximation of time consuming models for their use in optimization frameworks

Several widely used model optimization techniques such as, for instance, genetic algorithms, exploit an intelligent test of different input variables configurations. Such variables are fed to an arbitrary model and their effect is evaluated in terms of the output variables, in order to identify their optimal values according to some predetermined criteria. Unfortunately some models concern real world phenomena which involve a high number of input and output variables, whose interactions are complex. Consequently the simulations can be so time consuming that their use within an optimization procedure is unaffordable. In order to overcome this criticality, reducing the simulation time required for running the model within the optimization task, a novel method based on the combination of clustering and interpolation techniques is proposed. This technique is based on the use of a set of pre-run simulations of the original model, which are firstly used to cluster the input space and to assign to each cluster a suitable output value within the output space. Subsequently, in the simulation phase, an ad-hoc interpolation is performed in order to provide the final simulation results. The proposed method has been tested on two complex models related to the steel making industry: the first one concerns the optimization of blast furnace, the other one the operation of a EAF scrap pretreatment plant. The proposed approach has obtained good results in terms of accuracy and time-efficiency