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Decision support methodology for sustainable smart energy systems integration
Full text linkThe global demand for energy is continuously increasing, and the carbon dioxide
production related to the energy sector represents a large share of the overall
anthropogenic greenhouse gas (GHG) emissions, since most of the energy needs
are still provided by fossil fuels. To achieve the energy efficiency targets set by EU for the 2030 an energy transition towards more sustainable energy sources is required. The challenge will be the integration of different energy sectors in a smart energy system (SMES). Adopting a circular economy perspective it will be possible to turn the view on waste starting considering them as an energy source allowing more interactions between different stakeholders while exploiting technologies for the reduction of the environmental impact. This change in perspective needs also a change in the paradigm while taking decision on the implementation of this kind of interventions. The aim of this thesis is to fill the gaps in the development of decision support tools aiding the stakeholders in those interventions where SMES are implemented with developing and sustainable solutions
Stakeholder-centric model for efficient district heating: Networks interconnection and industrial waste heat in north-eastern Italy
Full text linkIntegrating industrial waste heat recovery into future sustainable Smart Energy Systems
No full textIn order to achieve the ambitious objectives set by the European Union for the climate and energy goals, a transition towards a future sustainable energy supply is needed. The integration of the huge potential for industrial waste heat recovery into Smart Energy System (SES) represents a main opportunity to accomplish these goals.
To successfully implement this strategy, the adoption of a system approach is required, since all the several stakeholders’ conflicting objectives should be considered. To address this challenge, in this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of a SES involving an industrial facility as the waste heat source and the neighborhood with different characteristics and activities (residential, commercial, and institutional) as potential users by a district heating network, in the typical European city brown field context.
The model has been applied to an Italian case study, analysing heat recovery from a steel casting facility to satisfy the heating needs of the southern part of Udine Municipality through the realization of a district heating (DH) network. Different DH layout scenarios have been analysed, to consider the connection of the main current and future city areas and different clients.
Energy system modelling has been implemented in MATLAB®, while the genetic multi-objective optimization has been performed by modeFRONTIER®, which allows to mark the Pareto front of solutions and the Multi Criteria Decision Making (MCDM) post processing analysis.
Results show that the developed model allows to properly select the DH network set of users and system layout to fully exploit the available waste energy, in particular how involving a large and heterogeneous basin of clients leads to remarkable economic and environmental performances. Design configurations such as the best compromise for thermal energy storage capacity are also provided. Moreover, the multi-objective model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenshi
Corrigendum to “100% renewable wastewater treatment plants: Techno-economic assessment using a modelling and optimization approach” [Energy Convers. Manage. 239 (2021) 114214]
No full textRespirometry tests in wastewater treatment: Why and how? A critical review
Full text linkRespirometry tests are a widely employed method in the wastewater treatment field to characterize wastewater streams, assess toxic/inhibitory effects to the biomass, calibrate mathematical models. Respirometry can allow to fractionation the chemical oxygen demand (COD) in biodegradable and inert fractions, but also provide information related to biomass kinetics and stoichiometry through standardized laboratory techniques. Considering the increasing number of emerging contaminants detected in wastewater effluents, such as pharmaceuticals, personal care products and pesticides, respirometry can be a useful tool to promptly assess any toxic or inhibitory effect in wastewater treatment plant (WWTP) operations. Beside conventional activated sludge, in recent years respirometric methods have been applied to innovative fields, such as moving-bed bio-reactors (MBBRs), fungi and microalgae, exploiting natural remediation methods. In particular, respirometry application to microalgae, through the so-called photo-respirometry, has been investigated in the latest years in the treatment of high-loaded streams, allowing resource recovery in biomass form. In this work, respirometric methods are first introduced from a theoretical basis and then critically discussed by considering the experimental apparatus, the available characterization protocols and the fields of application; the most recent literature findings on respirometry are coupled with authors' experience in the field. A comparison between physicochemical methods and respirometry is made. The future research needed on the topic is finally outlined, including the coupling of respirometry with microbial community analysis, potentially leading to an enhanced process understanding, an extended respirometry utilization to get specific kinetic and stoichiometric parameters for modelling purposes, and a wider respirometry application as a diagnosis tool in WWTP operations
How optimally selecting among internal and external opportunities for waste heat valorization: a case study from the steel industry
No full textConcerning waste heat recovery projects, especially in the energy-intensive industrial sectors, facility managers face indeed the challenge of making the optimal strategic choice within the different waste heat recovery options. In this context only the two energy recovery options based on a smart energy system approach, namely, power generation through an Organic Rankine Cycle (ORC) unit (both for self-consumption and grid selling) and the exploitation of the generated heat transfer fluid to feed an urban DH network. The economic objective represents the main driver, although environmental objectives are becoming increasingly important, also thanks to the rising value of green marketing. Indeed, when both the potential demand from external users and the opportunity to produce electricity represent attractive options, in order to allow the facility manager to select the most suitable waste heat recovery option and to decide which project to endorse, a deeper insight on the sustainability performances of each potential waste heat recovery solution is required. The developed DSS framework has then been applied adopting a facility manager’s perspective, with the aim to investigate the economic, energetic and environmental performances of different options for waste heat recovery exploitation, thus allowing a strategic decision making for the endorsement of the related investments. The model application provided useful suggestions on the optimal configuration of the energy recovery system, i.e. the selection of the most suitable option for the exploitation of the recovered energy, also taking into account the possible combination of different technologies, their optimal sizing and the definition of the operational strategy
Assessing the Techno-Economic Feasibility of Waste Electric and Electronic Equipment Treatment Plant: A Multi-Decisional Modeling Approach
Full text linkNowadays, sustainable approaches to waste management are becoming critical, due to increased generation and complex physicochemical composition. Waste electric and electronic equipment (WEEE) management, in particular, is being given increasing attention due to the continuous augment in electronic equipment usage and the limited recycling rates. In this work, a multi-objective engineering optimization approach using a decision support system (DSS) was used to analyze the
feasibility of installing a WEEE treatment plant in the Friuli-Venezia Giulia region (Northeastern Italy), considering that most of the produced WEEE is currently exported outside the region. Meaningful economic and environmental parameters were considered in the assessment, together with current WEEE production and composition. Plant investment cost was in the range of EUR 7–35 M for a potentiality of 8000–40,000 ton of treated WEEE/yr, the lower bound corresponding to the WEEE
produced in the region. Payback time was 4.3–10 yr, strongly depending on the market’s economic conditions as well as on plant potentiality. Proper public subsidies should be provided for a plant treating only the locally produced WEEE, establishing a circular economy. The fraction of recovered materials was 78–83%, fulfilling the current EU legislative requirements of 80% and stabilizing around values of 80% for a higher washing machine fraction. An increase in personal computers may allow to augment the economic revenues, due to the high conferral fees, while it reduces the amounts of
recovered materials, due to their complex composition. CO2 emission reduction thanks to material recovery was in the range of 8000–38,000 ton CO2/yr, linearly depending on the plant potentiality. The developed DSS system could be used both by public authorities and private companies to preliminarily evaluate the most important technical, financial and environmental aspects to assess overall plant sustainability. The proposed approach can be exported to different locations and integrated with energy recovery (i.e., incineration of the non-recoverable fractions), analyzing both environmental and economic aspects flexibly
Demand-Response Application in Wastewater Treatment Plants Using Compressed Air Storage System: A Modelling Approach
Full text linkWastewater treatment plants (WWTPs) are known to be one of the most energy-intensive
industrial sectors. In this work, demand response was applied to the biological phase of wastewater
treatment to reduce plant electricity cost, considering that the daily peak in flowrate typically coincides
with the maximum electricity price. Compressed air storage system, composed of a compressor
and an air storage tank, was proposed to allow energy cost reduction. A multi-objective modelling
approach was applied by analyzing dierent scenarios (with and without anaerobic digestion, AD),
considering both plant characteristics (in terms of treated flowrate and influent chemical oxygen
demand, COD, concentration) and storage system properties (volume, air pressure), together with
the current Italian market economic conditions. The results highlight that air tank volume has a
strong positive influence on the obtainable economic savings, with a less significant impact held by
air pressure, COD concentration and flowrate. In addition, biogas exploitation from AD led to an
improvement in economic indices. The developed model is highly flexible and can be applied to
dierent WWTPs and market conditions
100% renewable wastewater treatment plants: Techno-economic assessment using a modelling and optimization approach
No full textRenewable energies are being given increasing attention worldwide, as they are able to reduce the dependence on depletable fossil fuels. At the same time, wastewater treatment is known to be a significantly energy-intensive sector, which could potentially exploit renewable energies conversion in different forms. This study investigated the feasibility to design high renewable share wastewater treatment plants through dynamic simulations and optimization, aiming to move towards greener and energy-wise wastewater remediation processes. The main aim of the work was achieved by integrating photovoltaic systems with wind turbines, multi-energy storage technologies, i.e., batteries and hydrogen systems, and reverse osmosis tertiary treatment to absorb the power production surpluses. It was supposed that, in the newly proposed scenario, most of the plant electricity need would be covered by renewable energy. The optimization problem was multi-objective and found the trade-off solutions between minimizing the net present cost and maximizing the renewable share. In the first part of the study, the model was developed and applied to a medium-scale Italian municipal wastewater treatment plant. Model generalization was successively accomplished by applying the model to different locations and plant scales across the world. For all the investigated scenarios and cases, the optimal system integration was to design a renewable and storage system with a renewable share of 70%, corresponding to the lowest net present cost. The developed model is highly flexible and can be applied to other relevant case studies, boosting for a more sustainable wastewater treatment sector, enhancing at the same time local renewable energy conversion
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