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TOP-REF Report Summary

Project ID: 604140
Funded under: FP7-NMP
Country: Spain

Periodic Report Summary 2 - TOP-REF (Innovative tools, methods and indicators for optimizing the resource efficiency in process industry)

Project Context and Objectives:
The European Union is facing major challenges such us the economic crisis, sustainability and the competition with emerging economies. In this context, the TOP-REF project is willing to contribute to a smart and sustainable growth while promoting a more efficient, greener and competitive economy based on knowledge and innovation . The activities and the implementation of the results of TOP-REF will improve the resource efficiency within the EU intensive industry. Specifically, it is expected a rise in the efficiency in the use of energy, water and raw materials by up to 20%, while reducing production costs by up to 15%. Furthermore, it will contribute towards achieving the targets set by the European industry association SPIRE, which aims to reduce the use of non-renewable raw materials by 20% and of fossil fuels by 30%.
Moreover, one of the initiative key goals consists in considerably reducing the environmental impact linked to these processes, which takes the form of CO2 emissions, harmful substances and waste generation, among others.
Ultimately, the final goal is to contribute to drive Europe to a leadership position in energy efficiency in industries by means of the promotion of a more efficient, greener and competitive economy based on knowledge and innovation.
To achieve these goals, TOP-REF will develop and demonstrate a robust, resource-efficiency focused and cross-sectorial methodology. This methodology will be validated by the development and testing of non-invasive, real time and on-line monitoring and control tools adapted to three specific energy and resource intensive processes from the Fertilizer, Refining and Chemical sectors
In order to guarantee TOP-REF impacts, specific Key Resources Indicators (KRI) will be developed and standardised to foster the greening and the competiveness of the European process industry. The indicators, based among others on life cycle studies, will help to measure the decoupling of environmental impacts from the economic growth and the use of resources.
In the figure aside may be found a diagram with the roadmap of the methodology driving to the development of the Monitoring and Control tool.

Contributions to the roadmap of SPIRE
TOP-REF is directly linked with SPIRE, in this sense the activities of the project support and enhance the implementation of several Key Actions (KA) of the roadmap of SPIRE:
The specific contributions to each Key Action of the roadmap are included in the project deliverables in a dedicated section. The main KAs which the project is contributing to are included below.
• KA 2.3: Process monitoring, control and optimization. Methodology tools and indicators addressing specifically resource efficiency in industry
• KA 2.4: More efficient systems and equipment.
• K A 2.5 New energy and resource management concepts (including industrial symbiosis)
• KA 4.1: Systems approach: understanding the value of waste streams
KA 5.2: Methodologies and tools for cross-sectorial Life Cycle and Cost Assessment as well as novel social Life Cycle Assessment of energy and resource efficiency solutions.

Project Results:
During the first period of the project the following outcomes were achieved:
• First set of KRIs, including an innovative exergy indicator. The headline KRIs are: Material Consumption(kg/FU ), Direct Primary Energy Consumption (J/FU), Gross Water Use (m3/FU), Net Water Use (m3/FU) and the Resource Exergy Indicator (resources: materials, energy and water) (J/FU).
• Development of a novel methodology for audit and diagnosis, basis for a proper and homogeneous methodology to audit and diagnose the resource efficiency of the processes. This methodology, which is based on Thermoeconomics , once fully applied will provide the information to support a better decision making process to tackle the implementation of more efficient systems and equipment.
These results, have allowed achieving, during the second period, deeper outcomes focused on the usability and get a wider scope for the industry:
1. Global methodology for characterization and optimization of the processes by modeling and simulation
A general methodology for optimization of the process by modelling and simulation has been developed within this second period of the project. Firstly, the Key Resource Indicators (KRIs), parameters to represent resource efficiency indexes, were developed during the first period. They have been designed to be universally applicable to industrial sectors or plants, have been reviewed, updated and applied to the industrial sites.
In addition, a set of Key Performance Attributes (KPAs) was specifically defined for each plant to ensure a proper investigation space in terms of product quality, operability, safety and environmental issues. Next to these indicators, Process Models of the different use cases were set up by using commercially available process simulation softwares.

Overview of the TOP-REF methodology

Process models, KRIs and KPAs are the basis of the TOP-REF methodology approach. The final objective is to detect the influential Process Parameters that decrease resource efficiency (minimize the KRIs) using the KPAs as constraints. However, due to the large number of real plant operating parameters, the Critical Process Parameters (CPPs), have to be identified by a Global Sensitivity Analysis. The use of detailed process models, such as those used under TOP-REF, adds reliability but makes the sensitivity analysis and optimization algorithms highly computationally-intensive. In consequence, prior to the global sensitivity analysis, surrogate models are used. Surrogate models are sampling-based approximations of detailed process models which require less computational time, which enables the application of the global sensitivity analysis and the optimization tool. Thus, based on the detailed process models, surrogate models are developed and used for global sensitivity analysis. The global sensitivity analysis identifies the CPPs of all the operating parameters. In a last step, this set of CPPs is used to optimize the surrogate models by using Single and Multi-objective optimization techniques.
2. Ad-hoc plant models integrating Key Resource Indicators (KRIs) and Key Process Atributes (KPA)
Within TOP-REF framework, five modelling tools have been mainly used:

1. Chemical process simulators with advanced kinetic models (CPS)
2. Advanced Steam Network Modelling (ASNM)
3. Solid Handling Simulations (SHS)
4. Multi-scale Balance Population models in rotary drum for Solids (BPMS)
5. Surrogate Modelling (SM)
The models have been properly validated according to historical data from process plants. To ensure a proper operating space the KPA and KRI list has been reviewed, updated and a list of Process Parameters has been built for the three industrial cases.
Detailed process plant models of the different use cases (representing examples of the Agrochemical, Chemical and Petrochemical sectors) have been built by using commercially available process simulation softwares. The developed simulation tools describe in detail the components of the process units and utilities. In the case of the Fertilizer plant, the main process units have been properly simulated by default Aspen Plus® models, although for the rotary drum granulator-dryer and cooler, specific models based in population balance equations have been developed in MATLAB® and connected with the Aspen Plus® model by means of intermediate Excel® files.
The DCI use-case is the representative case for the chemical industry, which consists of a steam cracker and the associated compressors train plant and the corresponding utility plant. They have been separately modelled using Aspen Plus® software. Both models have been adequately validated and have to be integrated in order to generate data for building up a surrogate model.
As a third use-case, the Petrogal plant is the example of the petrochemical sector. It is a complex plant where the main energy-intensive process sections have been modelled in detail: the crude distillation unit, the fractionation section and the utility section. In this case, in order to obtain a more reliable model for the crude distillation unit, Aspen Hysys® has been adopted as the simulation software, instead of Aspen Plus®, which has been used only for the utility section. In a similar way to DCI case, both models have to be linked in order to obtain needed data for the surrogate model.

Potential Impact:
Impact on the industry
The implementation of the modelling methodology has provided the following impacts on the TOP-REF process
• Better understanding of the behavior and inertia of the ethylene cracker steam network and their interaction with the process with the simulations and calculations made so far within the scope of TOP-REF, has allowed to identify and suspect where the main inefficient spots are by yielding potential savings of about 3 MM€/yr. This figure comes from the expected optimization of the steam network Main outcomes achieved in the second period of the project control, by continuously adjusting their operating conditions (operating pressure and temperature at all steam levels) leading to minimize unbalances and steam venting to atmosphere.
• Reduction of gas consumption in the range 2% - 4% and power consumption 2-3 %. After the optimization of the processes, it would be possible to increase the reduction of gas and power leading to an estimated saving of 350.000-400.000 € per year in the drying and transport process of the granulated fertilizer in Fertinagro production plant located in Utrillas (Utrillas plant).
In addition, the model of the granulator allows a 10% reduction in the use of raw materials as it enables reducing the recirculation rate. This holds a high replication potential in all the plants of FERTINAGRO and other with similar processes.
• Due to the high complexity of the refinery the simulation work to be done had to be simplified and narrowed to a more specific part, which reduces the applicability of the models. Also, as a result of the competitive nature of the refining business, this sector is constantly implementing energy efficiency measures in order to keep the refining complex more up to date, making it more difficult to identify new improving opportunities. Regardless, the generation of an intricate model that combines both the crude distillation column and the utility section of Sines’s refinery, the two main focal points for energy efficiency improvement, will contribute to a new understanding of the system on an integrated way, as well as give basis to new projects to be tested and their impacts studied before being implemented.

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