Waste reduction algorithm

To address the ideal including environmental impact considerations into process design, Cabezas et al. [5] introduced a potential environmental impact (PEI) balance as an amendment of the Waste Reduction Algorithm [6]. However, this algorithm is simply a tool to be used to aid in evaluating the environmental friendliness of a process [7]. 

For analyzing the potential impact of a conceptual design we make use of the package WAR (Waste Reduction Algorithm) developed by the Environment Protection Agency in the USA [23]. The graphical interface allows the user to enter information for different alternatives, or to import it from a process simulator. Input data includes the chemicals species, the flow rates of the charac-... 

WAR (WAste Reduction Algorithm), http //www.epa.gov/oppt/greenengineering/software.html... 

Figure 3.5 Potential environmental impact (PEI) profiles for amounts of pollutants with biodegradation described by half-life in the water compartment. Bold lines represent mercury process pollutants light gray lines represent diaphragm process pollutants. PEI have been calculated using the waste reduction algorithm to present human toxicity potential by ingestion.

H. Cabezas, J.C. Bare, S.K. Mallick, Pollution prevention with chemical process simulator the generalized waste reduction (WAR) algorithm, Comput. Chem. Eng. 21 (1997) S305-S310. 

D.M. Young, R. Scharp, H. Cabezas, The waste reduction (WAR) algorithm environmental impacts, energy consumption, and engineering economics, Waste Manage. 20 (2000) 605-615. 

Fig. 30. Schematic design of a simple but very useful and efficient data reduction algorithm. Data representing the time trajectory of an individual variable are only kept (= recorded, stored) when the value leaves a permissive window which is centered around the last stored value. If this happens, the new value is appended to the data matrix and the window is re-centered around this value. This creates a two-column matrix for each individual variable with the typical time stamps in the first column and the measured (or calculated) values in the second column. In addition, the window width must be stored since it is typical for an individual variable. This algorithm assures that no storage space is wasted whenever the variable behaves as a parameter (i.e. does not change significantly with time, is almost constant) but also assures that any rapid and/or singular dynamic behavior is fully documented. No important information is then lost...

D. M. Young and H. Cabezas, Designing Sustainable Processes with Simulation The Waste Reduction (WAR) Algorithm, Computers Chemical Engineering, 23(10), 1477-1491... 

Phases I to n have covered planning and undertaking waste audit, resulting in the preparation of a material balance for each unit operation. Phase IV represents the interpretation of the material balance to identify process areas or components of concern. Figure 4 represent a material balance algorithm for the textile industry in establishing waste reduction options. 

To evaluate attributes for a process, the waste reduction (WAR) algorithm was developed initially as a pollution balance [46] and later as a potential impact balance [47]. The potential impact balance is a relative of toxicity indexes (e.g., [48]), which divide pollution by an LD50 (lethal dose that kills 50% of a test population) or threshold-limiting value. Flowever, the balance incorporates an input—output calculation of system analysis... 

Young DM, Cabezas H. Designing sustainable processes with simulation the waste reduction (WAR) algorithm. Compt Chem Eng 1999 23 1477-91. 

Young et al. (2000) developed environmental impact factors calculated using the Waste Reduction (WAR) algorithm. These factors encompass (1) physical potential impacts (acidification of soil, greenhouse enhancement, ozone depletion, and photochemical oxidant depletion), (2) human toxicity effects (air, water, and soil), and (3) eco-toxicity effects (aquatic and terrestrial). The important parameters are as follows ... 

To avoid this waste of time and money, and also to use those samples that have the largest spread of variations not only in those variables whose values are known but also in those variables that we may not even know exist but that have an effect on the spectrum, what is needed is a method of selecting samples that has certain characteristics. Those characteristics are as follows (a) It is based on measurements of the optical data only, and (b) it selects the samples that show the most differences in the spectra. An algorithm based on Gauss-Jordan reduction has been developed to overcome this problem [7], and here we present an approach based on the Mahalanobis distance concept. 

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