LCA modeling of chemical processes and materials presents many challenges but can identify important opportunities for reducing impacts. The presentations in this session address the following topics:

  •  Identifying opportunities to improve resource efficiency through productive re-use of surplus heat within chemical industry processes,
  • Enhancing LCIA modeling for personal care products by adding assessment of near-field impacts associated with product use and disposal, and
  • Using a macro-level technology assessment model to evaluate emerging chemical technologies and identify those with the greatest potential for reducing energy consumption and GHG emissions.
14:00 Nicolas DenzMichael BrunsMandy WescheAndré PaschetagStephan Scholl and Mieke Klein

Combining Material Flow Models and Heat Integration for Integrated Resource Efficiency Analysis in the Chemical Industry
SPEAKER: Mieke Klein

ABSTRACT. Resource efficiency analysis of production processes is an important basis for the reduction of climate impacts and costs in the chemical industry. A large number of modeling and analysis techniques exist to guide the reduction of resource consumption and the assessment of related ecological and economic impacts.

The consortium project InReff (‘Integrated Resource Efficiency for Reducing Climate Impacts in the Chemical Industry’, founded by the German Federal Ministry of Education and Research, BMBF) strives to combine modeling and analysis techniques from different fields for a holistic view upon resource efficiency analyses. This includes material flow networks [1], life cycle analysis [2], flow sheet simulation [3], heat integration [4], material flow cost accounting [5], and mathematical optimization [6]. The approach is guided by a developed methodology and supported by an IT-based modeling platform [7]. Project partners from the chemical industry successfully applied parts of the framework on practical case studies; see e.g. [8, 9, and 10].

This paper’s focus is on the combination of heat integration analysis and material flow networks. Techniques for heat integration analysis, such as Pinch [4] or specialized optimization algorithms [e.g. 11], allow the identification of theoretical potentials and utilization of technical aspects (i.e. heat exchanger networks) to productively re-use heat surplus in production processes in order to reduce resource consumption and climate impacts.

Material flow networks are an appropriate tool for process modeling in a more descriptive form than typical heat stream tables. Furthermore, they enable ecological and economic impact assessment of alternative scenarios identified in heat integration analyses; taking into account the whole production process or product life cycle.

For an iterative, model-based improvement of resource efficiency, a seamless integration of the involved software tools is an important precondition. This contribution will present a conceptual and technical interface as well as specific modeling aids to couple material flow networks [12] with different exemplary tools for heat integration analysis [13, 14]. The applicability of the integrated analysis will be shown on the example of a demonstrative case study from the chemical industry.


[1] Möller A., Page B., Rolf A., Wohlgemuth V. (2001): Foundations and Applications of Computer Based Material Flow Networks for Environmental Management. In: Rautenstrauch, C., Patig, S., editors. Environmental Information Systems in Industry and Public Services. London: Idea Group, pp. 379–396.

[2] Baumann H., Tillman A.M. (2004): The Hitch Hiker’s Guide to LCA: An Orientation in Life Cycle Assessment Methodology and Application, Studentlitteratur AB, Lund (Sweden).

[3] Seider W.D., Seader J.D., Lewin D.R., Widagdo S.: Product and Process Design Principles: Synthesis, Analysis and Design. 3rd edition, John Wiley & Sons Inc., Hoboken, 2008.

[4] Kemp I.C. (2011): Pinch Analysis and Process Integration: A User Guide on Process Integration for the Efficient Use of Energy. Butterworth-Heinemann.

[5] Tachikawa H. (2014): Manual on Material Flow Cost Accounting ISO 14051. ISBN 978-92-833-2450-8, Asian Productivity Organization, Tokyo (Japan).

[6] Nickel S., Stein O., Waldmann K.-H. (2014): Operations Research. 2nd edition, Springer / Gabler, Berlin / Heidelberg, ISBN 978-3-642-54367-8.

[7] Viere, T. et al. (2014): Integrated Resource Efficiency Analysis for Reducing Climate Impacts in the Chemical Industry. In: Journal of Business Chemistry, June 2014. http://www.businesschemistry. org/article/?article=192, last visit 2015-4-30.

[8] Denz N., Ausberg L., Bruns M., Viere T. (2014): Supporting resource efficiency in chemical industries – IT-based integration of flow sheet simulation and material flow analysis. Proceedings of the 21st CIRP Conference on Life Cycle Engineer-ing, Trondheim (Norway), June.

[9] Zschieschang E., Lambrecht H., Denz N., Viere T. (2014): Resource efficiency-oriented optimization of material flow networks in chemical process engineering, in: Proceedings of the 21st CIRP Conference on Life Cycle Engineering, Trond-heim (Norway), June.

[11] Brandt C., Fieg G., Luo X., Liu X. (2011): Improving genetic algorithms for the synthesis of heat exchanger networks. World Congress on Engineering and Technology (CET), Shanghai.

[12], last visit 2015-4-30.

[13], last visit 2015-4-30.

[14], last visit 2015-4-30.

14:15 Olivier JollietPeter FantkeLei Huang and Alexi Ernstoff

Accounting for near-field exposure to chemicals in consumer products in LCA

ABSTRACT. Every consumer product has the potential to expose humans to chemical ingredients during use, via multiple exposure pathways. However, many product oriented exposure assessment accounts mostly for indirect environmental exposure and not direct exposure of consumer to product during use. We therefore aim to a) Identify the most efficient interface between LCI and LCIA for the use and disposal stage of consumer products, b) determine the chemical concentration in product as an LCI input, c) define and calculate the Product Intake Fraction (PiF), a metric that accounts for near field exposure in LCA:, d) demonstrate the framework and models through examples from various consumer products. We propose to first determine the mass of chemical in product per functional unit (FU) as inventory flow and point of departure to then calculate intake and impact. This inventory flow is the amount of product used per FU multiplied by the chemical content in product. This content is either based on measured data, derived from household product databases, or determined based on chemical -product specific function and frame formulations. Intakes are then determined using the product intake fractions – the fraction of the chemical in product that is taken in via each exposure pathway, considering the specific point of entry (cosmetics, chemical in article, indoor air, etc.). We propose a new near & far field multi-media matrix of transfer fractions, with one column and row for each point of entry, for each environmental compartment and for each exposure pathway. The multiple transfer and product intake fraction (e.g from chemical in article to inhalation of indoor air) is obtained by inverting the transfer fraction matrix, yielding the infinite multi-media transfer fractions. Product intake fraction range from 10e-7 for an SVOC in a thick flooring, to 5e-3 for an indoor air emission up to 96% for a leave-on cosmetic ingredient.


Near-field exposure factor modeling of chemicals in personal care products

ABSTRACT. It has been estimated that there are thousands of chemicals used in personal care products (PCPs) and human exposure to these chemicals can be dominated by the use stage. Within chemically mediated human health (HH) life cycle impact assessment (LCIA), focus has historically been on far-field environmentally mediated exposures with less focus on exposure occurring in the near-field (e.g. in the indoor environment) during the product use stage. We used the concept of the product intake fraction (PiF) [1] to estimate near- and far-field exposure factors associated with chemicals in PCPs. We combine these with effect factors available in USEtox [2] to calculate characterization factors (CFs) for a subset of PCP chemicals and compare them to CFs associated with far-field emissions.

The PiF can be used to quantify the amount of chemical taken in per mass of chemical used in a PCP and can be combined with product composition to estimate chemical intake due to product use. The use-stage PiF for PCPs was estimated using mass balance modeling with physicochemical properties and product usage characteristics (e.g., leave-on or wash-off) as data inputs for dermal aqueous uptake, gaseous dermal exposure, and inhalation. Disposal stage PiFs were estimated by combining the fraction of chemical washed down the drain after product use and USEtox calculated far-field intake fractions. Additionally, to understand which physicochemical properties drive use-stage exposure, we calculated PiFs for a range of properties.

Varying physicochemical properties indicates the PiF is dominated by dermal aqueous uptake when a chemical has a relatively large Kow (octanol-air partition coefficient) combined with a relatively small Kaw (air-water partition coefficient). Furthermore, use-stage PiFs were 4-100% and 0.001-100% for leave-on and wash-off products, respectively, indicating a variability of about five orders of magnitude across PCP chemicals. Disposal-stage PiFs were 0-0.3% and in general were several orders of magnitude smaller than use-stage PiFs, however, some chemicals had comparable use- and disposal- stage PiFs. CFs for PCP use thus may have substantially different ranking compared to CFs for far-field releases and may dominate overall impact scores.

1. Jolliet et al., 2015, Environmental Science & Technology, Submitted. 2.


A Case Study of MAMTech Assessment Model: Prospective Life-cycle Technology Assessment of Future U.S. Ethylene Production

ABSTRACT. MAMTech (Macro-level Technology Assessment Model) is a modeling framework integrating chemical process design and modeling, life cycle assessment and techno-economic analysis for assessing the net energy and emissions implications of technology changes in the U.S. chemical production from a life cycle perspective. This assessment is critical because it can provide policy makers with good references on future investment and technology promotion, provide manufacturers and researchers with better understanding on technology potentials, possible bottlenecks and directions for future improvements. However, the assessment of new technology is challenging for lack of process data, general evaluation approach across different products and robust methodology over the temporal scale[1]. MAMTech is designed to overcome these barriers.

In this work, MAMTech is used to evaluate the macro-level life cycle energy and GHG emissions of several emerging technologies for ethylene production; in order to (1) demonstrate the function of MAMTech; (2) provide prospective insights on the energy and emissions reduction potentials of different technologies for ethylene production in future decades. Ethylene is chosen because it is one of the largest energy consumer and GHG emissions resources in chemical industry. According to IEA’s analysis, ethylene accounts 13% of global energy consumption and 15% of GHG emissions[2, 3].

Based on the preliminary results, 100-150 million GJ/year of energy consumption can be saved in the U.S. ethylene production life cycle under a high feedstock supply scenario through a new catalysis-based technology for steam cracker. This reduction of energy consumption mitigates 7-18 million ton of CO2-e/year. The ranges given here are based on results of Monto Carlo simulation considering uncertainties and system variances. Regarding the emerging technology that is not for improving current process but an alternative production pathway, MAMTech is able to overcome the knowledge barriers and provide a credible assessment by integrated modules of life cycle assessment and chemical process design. The results of different technologies are later compared together at different scenarios.

The results can shed the light on technology opportunities with the largest reduction potentials of energy and GHG emissions at a foreseeable time frame. The modeling framework itself is an effective tool assisting policy making, environmental and energy analysis, and R&D.

References [1] EIA. (01/02). The National Energy Modeling System: An Overview Available: [2] Y. Yao, D. Graziano, M. Riddle, J. Cresko, and E. Masanet, “Greener pathways for energy-intensive commodity chemicals: opportunities and challenges,” Current Opinion in Chemical Engineering, vol. 6, pp. 90-98, 11// 2014. [3] IEA, ICCA, and DECHEMA, “Technology Roadmap, Energy and GHG Reductions in the Chemical Industry via Catalytic Process,” IEA, ICCA, DECHEMA, France2013.