Barbara Fornai

Process Integration in Energy and Carbon Intensive Industries Through Exploitation of Optimization Techniques and Decision Support

Process industries show an ever-increasing interest in reducing their environmental impact and energy consumption as well as maintaining an acceptable profit. This is particularly true for industries such as the steel one, which is among the highest energy consumers worldwide. Process modelling and optimization are techniques by which this problem can be effectively addressed, particularly if the overall system is optimised as a whole. In this article we describe a model for a discrete dynamic optimization of the process gas network in an integrated steel plant. The main sub-plants are modelled in order to calculate mass and energy balances in different scenarios of operation. The scenarios are then exploited within a multi-objective optimization problem, where cost and CO2 emissions are simultaneously minimised. The optimization is carried out by exploitation of evolutionary algorithms that enable a flexible problem formulation and to effectively generate a set of different trade-off solutions. Application of the model to an industrial case study results in an interesting potential for reduction of CO2 emissions and costs. The described optimisation model is embedded in a more general software tool to help the plant managers in their daily decision-making process.

Use of Clustering and Interpolation Techniques for the Time-Efficient Simulation of Complex Models within Optimization Tasks

Several widely used model optimization techniques such as, for instance, genetic algorithms, exploit on 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 a complex model of a blast furnace within an optimization problem and has obtained good results in terms of accuracy and time-efficiency of the simulation.

A Software Tool for Comparing the Economic Value of Different Scrap Mixes in Steel Production

The electric steel making cycle exploits steel scrap as primary raw material. Many different types of steel scrap are used, which differ in their contents of iron and other components (e.g. Zn coating, plastics, etc.). Depending on the kind of scrap, some pretreatment steps are required, which increase the actual costs of this material, but some valuable by products could also be extracted through this pre-treatment. Moreover, depending on the steel grade to be produced, some kinds of steel scraps can be more suitable as they convey also very costly micro-alloying elements which must be added in order to provide the steel with suitable properties. Therefore the real economic value of each kind of scrap is not always correctly estimated, as it depends on the steel to produce. In the paper a tool is presented, which supports an improved exploitation of the different kinds of steel scrap, in order to find the optimal scrap mix for each steel grade. This tool estimates and compares the steel total cost achieved by mixing different types of steel scrap and considering also the cost of energy and other raw materials.

Process Integration Analysis for Innovative Environmentally Friendly Recovery and Pre-Treatment of Steel Scrap

The use of zinc-coated steel (e.g. galvanized steel) in melting cycles based on Electric Arc Furnaces can increase the production of harmful dust and hazardous air emissions. A process to simultane ...

A novel Key Performance Indicator for measuring the competitiveness of materials production within the EU

Competitiveness is a crucial factor for the sustainable development of any country. In this study, competitiveness is investigated under the perspective of the capability of a country to be self-sufficient from the energy point of view and to produce and export materials or goods. A ranking of countries and a classification of the most competitive materials over a long period for a geographic area is established through a novel economic indicator: as an exemplar case, calculations have been carried out considering a sample of five European countries, which produce some plastic materials in 2006 and up to 2020.