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Highlights • Analysis of possible energy savings in the German chemical industry. • Uncertainty analysis of costs and potentials of energy saving measures. • Evaluation of marginal cost curves of energy savings with uncertainties. • Determination of important model parameters using sensitivity analysis. Abstract In 2014, 19.3% of Germany’s industrial final energy consumption could be allocated to the chemical industry. Energy efficiency measures with focus on the chemical industry could thus contribute to reaching the German goal of reducing greenhouse gas emissions. To achieve this goal, energy planners and industries alike require an overview of the existing energy efficiency measures, their technical potential as well as the costs for realizing this potential. Energy efficiency opportunities are commonly presented in marginal cost curves, which rank these measures according to specific implementation costs. Existing analyses, however, do not take uncertainties in costs and potentials sufficiently into account. The aim of this paper is to create a marginal cost curve of energy efficiency measures for the chemical industry in Germany, while quantifying the uncertainties of the results and identifying the most influential input parameters. The identification of energy efficiency measures and the quantification of the associated technical potentials and costs were identified based on literature data and own assessments. Based on these findings a cost curve was created for the current technical potential. This potential was found to be 24.4 PJ per year, of which 23 PJ had negative lifetime costs. To investigate the uncertainties of these results, Monte Carlo simulations were performed to quantify the standard deviations of the implementation potential and costs. Furthermore, a sensitivity analysis, based on Morris Screening and linear regression, was conducted in order to identify the most influential model input parameters. With the applied approach, it was shown that uncertainties have a non-negligible impact on the final energy saving potential and costs, as well as the shape of marginal cost curves. The standard deviation of the energy saving potential was found to be 3.1 PJ. Furthermore, it is possible to systematically prioritise efforts in refining data. [ABSTRACT FROM AUTHOR]

A detailed analysis of the Danish industry is presented in this paper using the energy and exergy methods. For the 22 most energy-intensive process industries, which represent about 80% of the total primary energy use of the industrial sector, detailed end-use models were created and analysed with data for the years 2006 and 2012. The sectoral energy and exergy losses, as well as the exergy destruction, were further established to quantify the potential for recovering and valorising heat otherwise lost. By also considering transformation processes occurring in the utility sector, the impact of using electricity and district heat in the industry is shown. The exergy efficiencies for each process industry were found to be in the range of 12–56% in 2012. However variations in the efficiencies within the sectors for individual process industries occur, underlining the need for detailed analyses. The exergy losses amounted to 3800 TJ for the same year. Meanwhile, the complete exergy losses, including the central production of heat and power, exceeded 8700 TJ. This analysis illustrates for the case study of Denmark how waste heat recovery potentials in the industrial sectors are found, by determining the sectors losses and exergy destruction. In addition the importance of applying a system analysis is shown, which corrects the site efficiencies for electricity and district heating use. The use of 22 industries, further highlights differences amongst industries belonging to the same sector. [ABSTRACT FROM AUTHOR]

Numerous process integration techniques were proved to be highly effective for identifying and estimating potential energy savings in the industry. However, they require high time and effort to collect and analyse process data. As a result, they do not constitute the common practice in the industry and opportunities for increasing the energy efficiency of industrial processes are missed. The paper presents a method, termed the " Energy-Saving Decomposition ", which is based on Process Integration techniques. It is intended for expeditiously outlining and promoting energy efficiency in the industry. Two screening tools, based on mathematical criteria and engineering experience, are employed for reducing the problem dimension before applying conventional design procedures. The first step disregards streams based on their contribution to the overall energy-saving potential, calculated utilising a novel energy-saving decomposition technique. The most promising network is then selected based on its energy-saving potential and size. The second step reduces the problem complexity further, employing economic considerations. This novel method was exemplified by application to a dairy factory: the outcomes and the method itself were compared to conventional Pinch Analysis techniques. The results showed that the developed method can simplify and reduce the time consumption of conventional Process Integration methods significantly, while identifying the most encouraging saving opportunities. The automatic algorithm allowed for reducing the problem size from 62 process streams of the existing plant to 22 streams requiring a computational time of only 135 s. The final retrofit design proposed was the same obtained with conventional Pinch Analysis, achieving a 23% reduction in the plant final energy consumption. • A novel expeditious method for process integration retrofit studies is presented. • A novel decomposition of the energy-saving potential allows a reduction in problem size. • A case study proves that the problem size is reduced from 62 to 22 streams with no loss in favourable design options. • The method can be automated and is fast to compute, requiring a few minutes. [ABSTRACT FROM AUTHOR]