Enhancing Life‐Cycle Inventories via Reconciliation with the Laws of Thermodynamics

Abstract

Obtaining reliable results from life‐cycle assessment studies is often quite difficult because life‐cycle inventory (LCI) data are usually erroneous, incomplete, and even physically meaningless. The real data must satisfy the laws of thermodynamics, so the quality of LCI data may be enhanced by adjusting them to satisfy these laws. This is not a new idea, but a formal thermodynamically sound and statistically rigorous approach for accomplishing this task is not yet available. This article proposes such an approach based on methods for data rectification developed in process systems engineering. This approach exploits redundancy in the available data and models and solves a constrained optimization problem to remove random errors and estimate some missing values. The quality of the results and presence of gross errors are determined by statistical tests on the constraints and measurements. The accuracy of the rectified data is strongly dependent on the accuracy and completeness of the available models, which should capture information such as the life‐cycle network, stream compositions, and reactions. Such models are often not provided in LCI databases, so the proposed approach tackles many new challenges that are not encountered in process data rectification. An iterative approach is developed that relies on increasingly detailed information about the life‐cycle processes from the user. A comprehensive application of the method to the chlor‐alkali inventory being compiled by the National Renewable Energy Laboratory demonstrates the benefits and challenges of this approach.

Empirical Comparison of Input‐Output Methods for Life Cycle Assessment

“…Preliminary studies on process‐based LCI have demonstrated its potential usefulness for identifying missing flows as well as correcting measurement errors (Hau et al. ; Yi and Bakshi ; Tan et al. ; Ardente et al.…”

“…Strategies have been proposed to obtain high‐quality data or to assess the impact of uncertainty, such as incorporating emission generation mechanisms (von Bahr and Steen ), assigning data quality indicators (Weidema and Wesnæs ), reconciling data with conservation laws (Hau et al. ), and conducting sensitivity analysis and Monte Carlo simulation (Van den Berg et al. ; Björklund ).…”

“…Many results become questionable because LCA studies cover a broad scope, with at least dozens of processes and thousands of numerical data points drawn from a variety of sources with a wide range of accuracies (Bretz 1998). Strategies have been proposed to obtain high-quality data or to assess the impact of uncertainty, such as incorporating emission generation mechanisms (von Bahr and Steen 2004), assigning data quality indicators (Weidema and Wesnaes 1996), reconciling data with conservation laws (Hau et al 2007), and conducting sensitivity analysis and Monte Carlo simulation (Van den Berg et al 1999;Björklund 2002). Lloyd and Ries (2007) and Reap and colleagues (2008) summarized multiple sources of uncertainties and methods to quantify or propagate them to the final results.…”

Hybrid Framework for Managing Uncertainty in Life Cycle Inventories

“…The authors demonstrate the method by applying it to the estimation of uncertainty in hypothetical systems created with virtual data. An important recent development in the treatment of parameter uncertainty is the approach of Hau and colleagues to apply mass balance and thermodynamic principles to check and rectify LCI data sets (Hau et al 2007).…”

“…Mass balance applied to materials I-O of processes is the standard tool. More recently, Bakshi and colleagues have developed a promising approach to rectify/correct process data using both mass balance and thermodynamic laws (Hau et al 2007).…”