The purpose of this thesis has been to increase the transparency of LCA(Life Cycle Assessmemt) as a decision-supporting tool and to improve the scientific basis of LCA, with the greatest focus on the chemical processes. Improving the scientific basis of LCA thus does not mean that new scientific theories are proposed or empirical experiments performed but that a method is designed based - as far as possible on the present status of relevant scientific disciplines.
First, a LCA framework is proposed which distinguishes four different components: goal and scope definition, inventory, impact assessment and interpretation and the framework which is now in development in ISO. As far as possible, the ISO LCA framework and terminology is followed in this thesis. Goal & scope definition is concerned with defining the goal of the study in relation to the intended application. Application of LCA always involves some kind of comparison, for which a unit of use should be specified which is to form the basis for comparison. The unit is based on the function of the products to be compared, and is called the functional unit. In order to make a quantified survey of the environmental inputs and outputs of a product system, the boundary between the designated product system and other product systems must be determined, and some cut-off point set for the infinite regression of processes needed to produce inputs for other processes. Classification and characterization in impact assessment and interpretation are also discussed. In the classification component, the resource extractions and emissions associated with the life cycle of a product are translated into contributions to a number of environmental problem types, such as resource depletion, global warming, ozone depletion, acidification, and so on. To the end, each extraction and emissions is multiplied by a classification factor (or equivalency factor according to SETAC terminology) and the multiplication results are aggregated for each problem type. Valuation covers both valuation of the different environmental problem types and assessment of reliability and validity of the results. Interpretation identifies options to improve the products studied. Proposals on how to handle these issues are developed.
Next, a case study is performed to provide strategic tools with which the petrochemical industry can address the effective ways to improve environmental performance. This case study is to assess the energy use, water requirement, solid waste, and atmospheric and waterborne emissions generated by making the refinery product through a 'cradle-to-gate' analysis. Several challenges will be highlighted relating to the data collection and modeling phases of this investigation.
Next, a case study has been performed to provide energy LCI data by sources and comparative analysis of several demand sectors and LCA which will serve as basic data for national energy planning and policy development. The results elaborated in this study can be used for the development of consistent emission reduction policies for Korea and might also influence the decisions for implementation of emission reduction measures.
It is clear that allocation methods can be constructed on the company level. General principles defined in the framework are easily applicable and the methods lead to simple results.
Subsequently, a case study of fuel switching strategy which is linked in the chain of whole environmental strategy is conducted. This study shows how to the fuel switching strategy can be developed which is cost effective and to meet the target air pollutants concentration limits.
Last, the study presents an investment planning model for chemical processing networks using LCA as a tool for the quantification of environmental burden of chemical production. There are two objectives in the proposed model: the first objective is to maximize the net present value of the economic profit (NPV) over the planning horizon, while the second objective is to minimize the measure of the environmental burden which is quantified by LCA. By solving this multi-objective optimization model, investment decisions including process selection, installation timing/sizing and strategic shutdown of chemical processing networks over the given planning horizon are made. To effectively solve the multi-objective optimization model and to generate Pareto solutions, the e-constrained method is applied. The numerical results of the example shows that the proposed formulations provide a powerful tool for planners and process designers in order to establish a proper trade-off between environmental and economic objectives.
