Search

Industry poses one of the biggest challenges in the renewable energy transition. In this paper, fossil fuels in the European industrial sector are replaced by renewable energy using a novel tool, IndustryPLAN, a planning tool for the assessment of national industrial sectors. In a bottom-up approach, each industry sub-sector is addressed with energy efficiency and fossil fuel replacement measures based on best available and innovative technologies, and in a top-down approach, the fuel and electricity consumption per country is analysed and decarbonised. The results indicate that: 1. Known technologies can decarbonise most of the industrial sector; 2. Costs and efficiencies are improved by energy savings and electrification; 3. Limiting bioenergy consumption is a critical challenge, emphasising the key role of energy savings and electrification, and the alternative of using hydrogen or hydrogen-based electrofuels will make the transition more expensive and induce energy losses. A full transition to renewable energy and a decarbonised industry sector may be possible before 2050, however, this requires that all investments are sustainable from 2030 onwards and that grid electricity is fully decarbonised. This paper presents several pathways toward 100% renewable energy supply in the European industrial sector and discusses the implications of the outlined scenarios. • Four tangible 100% renewable energy scenarios for industry in EU27 + UK are established. • 100% renewable energy in industry is feasible before 2050. • Energy efficiency improvements are essential to limit biomass consumption. • Industry energy transition needs to be prioritised in short- and long-term planning. [ABSTRACT FROM AUTHOR]

Abstract: This paper will summarise the European Strategy for Energy and Climate Change. In current international negotiations Europe has proposed a 20% reduction in GHG (greenhouse gases) in the developed countries by 2020 or 30% should there be an international agreement in the domain. However it is important to define measures to achieve the targets. One of the principal tools is to improve energy efficiency under the energy efficiency action plan, which will help to achieve a 20% energy saving by 2020. On the other hand, the amount of energy from renewable sources consumed in Europe will have to rise from its current level of 8.5%–20% by 2020. These are ambitious but achievable targets. Nonetheless, these can only be achieved through strong investment in areas of the knowledge triangle which strengthens research and innovation in the energy sector in Europe. The paper covers European Energy and Climate Change Policy, the European Strategic Energy Technology plan, the consequences of the Lisbon Treaty, European and national Road maps to a low carbon economy, the Energy Efficiency Plan for 2011 and finishes with a brief consideration of the EU’s energy infrastructure priorities. [Copyright &y& Elsevier]

High energy efficiency for low operating cost of desalination can be obtained from operational optimizations as well as component-wise innovations. This paper is concerned with an energy-efficient operation for reverse osmosis (RO) desalination process involving a joint control of multiple modules. Energy resource considered for desalination is of two types: intermittent solar energy and steady grid energy. With solar energy, the energy efficiency is measured by total permeate production obtained while the solar energy is available. With grid energy, the energy efficiency is measured by the power consumed to get unit permeate production rate. Achieving maximum energy efficiency is equivalent to solving constrained optimization. For the constrained optimization, obligatory constraints are given by the permissible ranges of trans-membrane pressure on RO membrane and total dissolved solids of permeate, and optional constraint is given by the permissible range of permeate production rate. Constraints as well as the objective function are modeled by the empirical second order equations in terms of control variables of multiple modules. Jointly optimal values of the control variables are found by the sequential quadratic programming. Experimental results obtained by the jointly optimal control demonstrate superior performances as compared to those acquired by marginally optimal control schemes. [ABSTRACT FROM AUTHOR]