Production of fine and speciality chemicals: procedure for the estimation of LCIs

Abstract

To improve the environmental performance of chemical products or services, especially via comparisons of chemical products, LCA is a suitable evaluation method. However, no procedure to obtain comprehensive LCI-data on the production of fine and speciality chemicals is available to date, and information on such production processes is scarce. Thus, a procedure was developed for the estimation of LCIs of chemical production process-steps, which relies on only a small amount of input data. A generic input-output scheme of chemical production process-steps was set up, and equations to calculate inputs and outputs were established. For most parameters in the resulting estimation procedure, default values were derived from on-site data on chemical production processes and from heuristics. Uncertainties in the estimated default values were reflected as best-case and worst-case scenarios. The procedure was applied to a case study comparing the production of two active ingredients used for crop protection. Verification and a sensitivity analysis were carried out. It was found that the impacts from the mass and energy flows estimated by the procedure represent a significant share of the impacts assessed in the case study. In a verification, LCI-data from existing processes yielded results within the range of the estimated best-case and worst-case scenarios. Note that verification data could not be obtained for all process steps. From the verification results, it was inferred that mass and energy flows of existing processes for the production of fine and speciality chemicals correspond more frequently to the estimated best-case than to the worst-case scenario. In the sensitivity analysis, solvent demand was found to be the most crucial parameter in the environmental performance of the chemical production processes assessed. Mass and energy flows in LCIs of production processes for fine and speciality chemicals should not be neglected, even if only little information on a process is available. The estimation procedure described here helps to overcome lacking information in a transparent, consistent way. Additional verifications and a more detailed estimation of the default parameters are desirable to learn more about the accuracy of the estimation procedure. The procedure should also be applied to case studies to gain insight into the usefulness of the estimation results in different decision-making contexts.