Total Cost and Environmental Impact of Ownership — TCEIO

2013 ◽  
Vol 108 (1-2) ◽  
pp. 63-67 ◽  
Author(s):  
Rolf Steinhilper ◽  
Martin Andreas Süchting ◽  
Markus Kafara
2015 ◽  
Vol 88 ◽  
pp. 335-346 ◽  
Author(s):  
Joan Carreras ◽  
Dieter Boer ◽  
Gonzalo Guillén-Gosálbez ◽  
Luisa F. Cabeza ◽  
Marc Medrano ◽  
...  

2016 ◽  
Vol 13 (2) ◽  
pp. 149-162
Author(s):  
Sunday Olayinka Oyedepo ◽  
Richard Olayiwola Fagbenle ◽  
Samuel Sunday Adefila ◽  
Md Mahbub Alam

Purpose This study aims to use an environomics method to assess the environmental impacts of selected gas turbine power plants in Nigeria. Design/methodology/approach In this study, exergoenvironomic analysis has been carried out to investigate the environmental impact of selected gas turbine power plants in Nigeria from an exergetic point of view. Findings The exergy analysis reveals that the combustion chamber is the most exergy destructive component compared to other cycle components. The exergy destruction of this component can be reduced by increasing gas turbine inlet temperature (GTIT). The results of the study show that thermodynamic inefficiency is responsible for the environmental impact associated with gas turbine components. The study further shows that CO2 emissions and cost of environmental impact decrease with increasing GTIT. Originality/value The exergo-environomic parameters computed in this study are CO2 emission in kg per MWh of electricity generated, depletion number, sustainability index, cost flow rate of environmental impacts (Ċenv) in $/h and total cost rates of products (ĊTot) in $/hr. For the period considered, the CO2 emissions for the selected plants vary from 100.18 to 408.78 kgCO2/MWhm, while cost flow rate of environmental impacts varies from $40.18 /h to $276.97 /h and the total cost rates of products vary from $2935.69/h to $12,232.84/h. The depletion number and sustainability index vary from 0.69 to 0.84 and 1.20 to 1.44, respectively.


1989 ◽  
Vol 21 (8) ◽  
pp. 1015-1026
Author(s):  
A Koizumi ◽  
T Inakazu

In this paper the goal programming model is given that deals with the optimal scale and location of an area-wide sewerage system, consisting of trunk sewers, treatment plants, and pumping stations. The uncertainty of future wastewater volume is considered, and the regional objectives are evaluated. Hence, the model is composed of several constraints and of three objective functions (goals). The major constraints are the capacity of the treatment plants and the estimated range of wastewater amount in each municipality. The goals are to minimize total cost and environmental impact, while maximizing user satisfaction with the sewerage system. The case study shows that this model can help to decide the location and scale of facilities for sewerage system, giving results that are a good balance of the three goals. Furthermore, the analysis, by varying the settings of the goals or the values of sewerage propagation ratio, shows that the model can simulate changes in the wastewater capacity and its transport routes. Thus our model makes it easier for decisionmakers to plan area-wide sewerage systems.


1999 ◽  
Vol 38 (01) ◽  
pp. 50-55 ◽  
Author(s):  
P. F. de Vries Robbé ◽  
A. L. M. Verbeek ◽  
J. L. Severens

Abstract:The problem of deciding the optimal sequence of diagnostic tests can be structured in decision trees, but unmanageable bushy decision trees result when the sequence of two or more tests is investigated. Most modelling techniques include tests on the basis of gain in certainty. The aim of this study was to explore a model for optimizing the sequence of diagnostic tests based on efficiency criteria. The probability modifying plot shows, when in a specific test sequence further testing is redundant and which costs are involved. In this way different sequences can be compared. The model is illustrated with data on urinary tract infection. The sequence of diagnostic tests was optimized on the basis of efficiency, which was either defined as the test sequence with the least number of tests or the least total cost for testing. Further research on the model is needed to handle current limitations.


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