ارزیابی چرخه عمر یک کلکتور خورشیدی حرارتی: تجزیه و تحلیل حساسیت، موازنه انرژی و محیط زیست

Starting from the results of a life cycle assessment of solar thermal collector for sanitary warm water, an energy balance between the employed energy during the collector life cycle and the energy saved thanks to the collector use has been investigated. A sensitivity analysis for estimating the effects of the chosen methods and data on the outcome of the study was carried out. Uncertainties due to the eco-profile of input materials and the initial assumptions have been analysed. Since the study is concerned with a renewable energy system, attention has been focused on the energy indexes and in particular the “global energy consumption”. Following the principles of Kyoto Protocol, the variations of CO2 emissions have also been studied.

The life cycle assessment (LCA) is a useful tool to estimate the effective energy and environmental impacts related to products or services. However, the results of LCA do not represent “exact” and “precise” data, but are affected by a multitude of uncertainty sources. The reliability of LCAs strictly depends on complete and sharp data that unfortunately are not always available [1]. ISO 14040 recommends to investigate all those parameters that could heavily influence the final eco-profile [2]. Because Life Cycle Inventory (LCI) results are generally used for comparative purposes, the quality of data is essential to state whether results are valid or not [3], [4] and [5]. Regarding data quality, LCA studies should include: time-related coverage, geographical coverage, technology coverage, precision, completeness and representativeness of data consistency and reproducibility of methods used throughout the LCA, sources of the data and their representativeness, uncertainty of the information [2]. The international standards give little practical guidance on how to manage such information. In addition to previously listed parameters, other sources of uncertainty are [6]: – Data inaccuracy (due to errors and imperfection in the measurements); – Data gaps or not representative data; – Structure of the model (as simplified model to represent the functional relationships); – Different choices and assumptions; – System boundaries definition; – Characterisation factors and weights (as those used in the calculation of potential environmental impacts); – Mistakes (unavoidable in every step of LCA). Furthermore, the global environmental balance of a product is strictly related to the service life (“Period of time after installation during which all essential properties of an item meet or exceed the required performance” [7]) and durability (“Capability of an item to perform its required function over a period of time” [7]) concepts. The durability is certainly a key element since LCA takes the life cycle of the material into account, which includes its use over a number of years: by increasing the length of the service life, the use of resources is improved as much as specific impacts are reduced. Design concepts, aiming to improve the environmental performance of a product, should include the design for durability and the design for longevity including, for example, concepts of reparability, maintainability and upgradability [8] and [9]. However, even the durability assessment implies many problems and uncertainties as: non-reproducibility and traceability of field tracking studies, subjectivity of expert opinion, length of accelerated tests and natural weathering, relevance of stress test, required quality and quantity of knowledge for modelling [10]. Moreover, the study of uncertainty sources is itself affected by uncertainty. It is necessary to distinguish uncertainty, which arises due to the lack of the knowledge about the true value of a quantity, from variability that is attributable to the natural heterogeneity of values [6]. Uncertainty could be reduced by more precise and accurate measurements while variability is entailed into processes. Details contained in the normal LCI study do not often allow distinguishing uncertainty from variability. Consequently, in this study, they will be jointly considered. Starting from the results of the LCA applied to a solar thermal collector [11], [12] and [13], a sensitivity analysis (SA) has been carried out. This is a systematic procedure for estimating the effects on the outcome of a study of the chosen methods and data [14]. SA can be applied with either arbitrarily selected ranges of variation, or variations that represent known ranges of uncertainty. SA is an important element of judgement for the corroboration or the refutation of the scientific hypotheses embedded into a model. This is particularly critical when both model parameters and available data are affected by uncertainties (as occurs in LCAs). However, SA can also be used to direct the research priorities by focusing on the parameters which mostly determine the uncertainty of the model. The results presented in this work are extracted from the case study “CS2” performed within the works of Task 27—Subtask C of International Energy Agency (IEA) about “Performance, durability and sustainability of advanced windows and solar components for buildings”. The study follows three main steps: 1. Estimation of the energy and environmental balances: being concerned with a renewable energy system and following the principles of Kyoto Protocol, the attention of this study has been focused on the energy and CO2 indexes. In particular, the energy and the emission payback times have been calculated. 2. Study of the uncertainty sources: the eco-profile has been studied in detail. The study focused on the main uncertainty sources in order to estimate their influence on final results. Data quality regarding raw materials and the main LCA initial assumptions (as system boundaries or impacts allocation) has been analysed. 3. Incidence of uncertainties on the environmental indexes: following the previous considerations, the uncertainties of a parameter have been depicted as a variation range of the energy and environmental indexes. Analogously, a scenario analysis to describe the incidence of the different assumptions has been carried out.

The LCA studies have generally an intrinsic uncertainty related to various factors (i.e. difficulty in the survey of data, lack of detailed information sources, data quality, etc.). Consequently, it is more important for the experts to evaluate the order of magnitude of input–output flows ascribable to the product than to trace an “exact” eco-profile of products. This problem, commonly noticed in every LCA, has been strongly noticed in our case study. Regarding the solar thermal collector, we have observed a strong dependence of the FU eco-profile on the input materials. They globally imply about 70–80% of the environmental impacts. The environmental impacts of material have been supposed to be enclosed within a variation range. These intervals have been realised on the basis of data coming from environmental databases, LCA tools and, in general, from European environmental studies. The analysis of data quality has been based on many parameters such as geographical coverage, technological level, representativeness, etc. Results have shown a great uncertainty regarding aluminium, copper, thermal fluid and galvanized steel, the dominant material. Even the other life cycle steps (transports, installation and maintenance) cause large impacts. The production process affects the eco-profile for about 5% of impacts (excepting some air pollutants released during cutting and welding steps). The LCA results have been synthesised into two indexes: the energy and environmental payback times. The great energy and environmental convenience of this equipment are shown by very low values of payback times (lower than 2 years). Including the variability related to raw material eco-profiles and the uncertainties due to the other life cycle steps, it has been estimated that the variation range can be extended as following: energy consumption from 8 to 15 GJ, CO2 emission from 500 to 900 kg. Joining concepts of durability and supposing a loss of efficiency up to 40%, it has been estimated that, even in pessimistic scenarios, the energy and emission payback times are lower than 4 years. These results permit to state a positive qualitative judgement regarding the environmental performances of the collector that is not sensibly influenced by all the study uncertainties.