Decision support framework for developing cost-effective fuel-mix for power generation in Pakistan

Author(s):  
Muhammad Abbas Choudhary ◽  
Nawar Khan ◽  
Aisha Abbas
Author(s):  
Khanh Q. Bui ◽  
Lokukaluge P. Perera

Abstract Stringent regulations regarding environmental protection and energy efficiency (i.e., emission limits regarding NOx, SOx pollutants and the IMO greenhouse gases reduction target) will mark a significant shift to the maritime industry. In the first place, the shipping industry has strived to work towards feasible technologies for regulatory compliance. Nevertheless, life cycle cost appraisal attaches much consideration of decision-makers when it comes to investment decisions on new technologies. Therefore, the life cycle cost analysis (LCCA) is proposed in this study to evaluate the cash flow budgeting and cost performance of the proposed technologies over their life cycles. In the second place, environmental regulations may support innovation especially in the era of digitalization. The industrial digitalization is expected to revolutionize all of the aspects of shipping and enable the achievement of energy-efficient and environmental-friendly maritime operations. The so-called Internet of things (IoT) with the utilization of sensor technologies as well as data acquisition systems can facilitate the respective maritime operations by means of vessel operational performance monitoring. The big data sets obtained from IoT should be properly analyzed with the help of Artificial Intelligence (AI) and Machine Learning (ML) approaches. Our contribution in this paper is to propose a decision support framework, which comprises the LCCA analysis and advanced data analytics for ship performance monitoring, will play a pivotal role for decision-making processes towards cost-effective and energy-efficient shipping.


2020 ◽  
Vol 39 (3) ◽  
pp. 4631-4650
Author(s):  
Xiao-Yu Zhou ◽  
Xiao-Kang Wang ◽  
Jian-qiang Wang ◽  
Jun-Bo Li ◽  
Lin Li

With the rapid growth of the global population and economy, energy consumption and demad are increasing sharply. As an essential renewable energy, biomass energy can promote the reform of energy production and consumption. Considering the characteristics of long investment cycle and large investment scale of agroforestry biomass power generation (AFBPG) projects, this study establishes a decision support framework for risk ranking of AFBPG project under picture fuzzy environment. The proposed framework considers not only the fuzziness and uncertainty of decision-making problems but also the decision-makers’ (DMs) psychological behavior. First, given the integrity of information representation, DMs provide risk assessment information expressed with picture fuzzy numbers, and then gives the distance of the picture fuzzy set (PFS) to maximize the PFS information. Second, the entropy weight method is used to compute the objective weight. Third, the VIKOR (Vlse Kriterijumska Optimizacija I Kompromisno Resenje) – TODIM (an acronym in Portuguese for an interactive multi-criteria decision making) method is suggested for ranking risk factors, which reflects the behavioral psychology of DMs. Moreover, the proposed evaluation model is successfully applied in a practical case. The results show that the model is valid for ranking risk factors under picture fuzzy environment. Last but not least, comparison and sensitivity analysis are implemented to verify the effectiveness and applicability of the proposed method and some suggestions for practical application are put forward.


2015 ◽  
Author(s):  
L. K. Kirkman ◽  
John K. Hiers A. ◽  
L. L. Smith ◽  
L. M. Conner ◽  
S. L. Zeigler ◽  
...  

2001 ◽  
Vol 47 (1) ◽  
pp. 110-117 ◽  
Author(s):  
Magnus Jonsson ◽  
Joyce Carlson ◽  
Jan-Olof Jeppsson ◽  
Per Simonsson

Abstract Background: Electrophoresis of serum samples allows detection of monoclonal gammopathies indicative of multiple myeloma, Waldenström macroglobulinemia, monoclonal gammopathy of undetermined significance, and amyloidosis. Present methods of high-resolution agarose gel electrophoresis (HRAGE) and immunofixation electrophoresis (IFE) are manual and labor-intensive. Capillary zone electrophoresis (CZE) allows rapid automated protein separation and produces digital absorbance data, appropriate as input for a computerized decision support system. Methods: Using the Beckman Paragon CZE 2000 instrument, we analyzed 711 routine clinical samples, including 95 monoclonal components (MCs) and 9 cases of Bence Jones myeloma, in both the CZE and HRAGE systems. Mathematical algorithms developed for the detection of monoclonal immunoglobulins (MCs) in the γ- and β-regions of the electropherogram were tested on the entire material. Additional algorithms evaluating oligoclonality and polyclonal concentrations of immunoglobulins were also tested. Results: CZE electropherograms corresponded well with HRAGE. Only one IgG MC of 1 g/L, visible on HRAGE, was not visible after CZE. Algorithms detected 94 of 95 MCs (98.9%) and 100% of those visible after CZE. Of 607 samples lacking an MC on HRAGE, only 3 were identified by the algorithms (specificity, 99%). Algorithms evaluating total gammaglobulinemia and oligoclonality also identified several cases of Bence Jones myeloma. Conclusions: The use of capillary electrophoresis provides a modern, rapid, and cost-effective method of analyzing serum proteins. The additional option of computerized decision support, which provides rapid and standardized interpretations, should increase the clinical availability and usefulness of protein analyses in the future.


2021 ◽  
Vol 55 (5) ◽  
pp. 2890-2898 ◽  
Author(s):  
Tami C. Bond ◽  
Angela Bosco-Lauth ◽  
Delphine K. Farmer ◽  
Paul W. Francisco ◽  
Jeffrey R. Pierce ◽  
...  

Author(s):  
David Kik ◽  
Matthias Gerhard Wichmann ◽  
Thomas Stefan Spengler

AbstractLocation choice is a crucial planning task with major influence on a company’s future orientation and competitiveness. It is quite complex, since multiple location factors are usually of decision-relevance, incomparable, and sometimes conflictual. Further, ongoing urbanization is associated with locational dynamics posing major challenges for the regional location management of companies and municipalities. For example, respecting urban space as location factor, a scarcity growing over time leads to different assessment and requirements on a company’s behalf. For both companies and municipalities, there is a need for location development which implies an active change of location factor characteristics. Accordingly, considering locational dynamics is vital, as they may be decisive in the location decision-making. Although certain dynamics are considered within conventional Facility Location Problem (FLP) approaches, a systematic consideration of active location development is missing so far. Consequently, they may propagate long-term unfavorable location decisions, as major potentials associated with company-driven and municipal development measures are neglected. Therefore, this paper introduces a comprehensive decision support framework for the Regional Facility Location and Development planning Problem (RFLDP). It provides an operationalization of development measures, and thus anticipates dynamic adaptations to the environment. An established multi-criteria approach is extended to this new application. A complementary guideline ensures its meaningful applicability by practitioners. Based on a real-life case study, the decision support framework’s strength for practical application is demonstrated. Here, major advantages over conventional FLP approaches are highlighted. It is shown that the proposed methodology results in alternative location decisions which are structurally superior.


Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4402
Author(s):  
Chun-Kai Wang ◽  
Chien-Ming Lee ◽  
Yue-Rong Hong ◽  
Kan Cheng

Energy transition has become a priority for adaptive policy and measures taken in response to climate change around the world. This is an opportunity and a challenge for the Taiwan government to establish a climate-resilient power generation mixed to ensure electricity security as well as climate change mitigation. This study adopted a sustainable development perspective and applied optimal control theory to establish a cost-effective model to evaluate a long-term (2050), climate-resilient power generation mix for Taiwan. Furthermore, this study applies the STIRPAT approach to predict the demand of electricity by 2050 for the demand side management. The results not only showed the share of various power generation mixed, but also recommended the trajectory of electricity saving by 2050.


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