An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer

The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed. However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed. A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility of the model and the developed algorithm

Exploring multi-objective trade-offs in the design space of a waste heat recovery system

A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy. In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large. To address this problem, a multi-objective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clusteringalgorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal designs. As a case study, a simulation of a WHRS commonly found in the foodand drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system’s parameters and their trade-offs, providing another source of information for prospective installations

Energy-efficient scheduling of flexible flow shop of composite recycling

Composite recycling technologies have been developed to tackle the increasing use of composites in industry and as a result of restrictions placed on landfill disposal. Mechanical, thermal and chemical approaches are the existing main recycling techniques to recover the fibres. Some optimisation work for reducing energy consumed by above processes has also been developed. However, the resource efficiency of recycling composites at the workshop level has never been considered before. Considering the current trend of designing and optimising a system in parallel and the future needs of the composite recycling business, a flexible flow shop for carbon fibre reinforced composite recycling is modelled. Optimisation approaches based on non-dominated sorting genetic algorithm II (NSGA-II) have been developed to reduce the time and energy consumed for processing composite wastes by searching for the optimal sub-lot splitting and resource scheduling plans. Case studies on different composite recycling scenarios have been conducted to prove the feasibility of the model and the developed algorithm

Multi-scale modelling and optimisation of sustainable chemical processes

This dissertation explores the process modelling and optimisation of chemical processes under sustainability criteria. Resting on process systems engineering techniques combined with life cycle assessment (LCA), we present implementation strategies to improve flowsheet performance and reduce environmental impacts from early design stages. We first address the relevance of sustainability assessments in the sector and present process and environmental modelling techniques available. Under the observation that chemical processes are subject to market, technical, and environmental fluctuations, we next present an approach to account for these uncertainties. Process optimisation is then tackled by combining surrogate modelling, objective-reduction, and multi-criteria decision analysis tools. The framework proved the enhancement of the assessments by reducing the use of computational resources and allowing the ranking of optimal alternatives based on the concept of efficiency. We finally introduce a scheme to assess sustainable performance at a multi-scale level, from catalysis development to planet implications. This approach aims to provide insights about the role of catalysis and establish priorities for process development, while also introducing absolute sustainability metrics via the concept of ‘Planetary boundaries’. Ultimately, this allows a clear view of the impact that a process incurs in the current and future status of the Earth. The capabilities of the methods developed are tested in relevant applications that address challenges in the sector to attain sustainable performance. We present how concepts like circular economy, waste valorisation, and renewable raw materials can certainly bring benefits to the industry compared to their fossil-based alternatives. However, we also show that the development of new processes and technologies is very likely to shift environmental impacts from one category to another, concluding that cross-sectorial cooperation will become essential to meet sustainability targets, such as those determined by the Sustainable Development Goals.Open Acces

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

Electricity and Water Cogeneration Utilizing Aluminium Furnaces Waste Heat Integrating Thermal Storage Organic Rankine Cycle

High energy-intensive industries, including steel, chemicals, cement, and aluminium, contribute to about 75% of the industrial emissions of carbon dioxide globally and expelling large amounts of unrecovered waste heat into the atmosphere. Yet, there has been a challenge of studies that are conducted on recovering waste heat in the aluminium industry, especially in cast-house facilities, due to technical difficulties such as energy fluctuations in mass flow rate and temperature. In this study, the waste heat to power system is designed to generate power and freshwater in a cast-house facility with 18 furnaces by evaluating three methods in which the temporal waste heat from holding furnaces can be damped and exploited. These methods are: (1) implementing a temporal air injection, (2) optimising furnaces operation time shift, and (3) integrating sensible thermal heat storage. Organic Rankine Cycle is used for the waste heat to power conversion. The appropriate thermal energy storage design and a thermodynamic model of an Organic Rankine Cycle are investigated using temporal flue gas data that are collected on site from three furnaces. Reverse Osmosis technology is applied to produce water using the generated electricity. Results show that sensible heat thermal energy storage is the most suitable technology for damping the fluctuations of waste heat. By utilising waste heat from 18 remelting furnaces, a net power output of 323 kW can be produced to operate a Reverse Osmosis plant supplying 2419 m3 of fresh water daily, saving up to 2000 metric tons of carbon dioxide emissions annually. This study gives a comprehensive approach to deal with temporal waste heat in aluminium furnaces for smooth cogeneration

Multiobjective Optimization of a Plate Heat Exchanger in a Waste Heat Recovery Organic Rankine Cycle System for Natural Gas Engines

A multiobjective optimization of an organic Rankine cycle (ORC) evaporator, operating with toluene as the working fluid, is presented in this paper for waste heat recovery (WHR) from the exhaust gases of a 2 MW Jenbacher JMS 612 GS-N.L. gas internal combustion engine. Indirect evaporation between the exhaust gas and the organic fluid in the parallel plate heat exchanger (ITC2) implied irreversible heat transfer and high investment costs, which were considered as objective functions to be minimized. Energy and exergy balances were applied to the system components, in addition to the phenomenological equations in the ITC2, to calculate global energy indicators, such as the thermal efficiency of the configuration, the heat recovery efficiency, the overall energy conversion efficiency, the absolute increase of engine thermal efficiency, and the reduction of the break-specific fuel consumption of the system, of the system integrated with the gas engine. The results allowed calculation of the plate spacing, plate height, plate width, and chevron angle that minimized the investment cost and entropy generation of the equipment, reaching 22.04 m2 in the heat transfer area, 693.87 kW in the energy transfer by heat recovery from the exhaust gas, and 41.6% in the overall thermal efficiency of the ORC as a bottoming cycle for the engine. This type of result contributes to the inclusion of this technology in the industrial sector as a consequence of the improvement in thermal efficiency and economic viability