scholarly journals Forecasting the ranks of sites suitable for power plant installations

2017 ◽  
Vol 15 (14) ◽  
pp. 7453-7471
Author(s):  
Dr. Kalyani Sambhoo(Salla) ◽  
Dr. Sanjay Kadam
Keyword(s):  
Water Quality ◽  
Decision Making ◽  
Air Quality ◽  
Power Plant ◽  
Soft Computing ◽  
Planning Tool ◽  
Soft Computing Techniques ◽  
Quality Water ◽  
Computationally Expensive ◽  
Ecological Suitability

An increase in the number of decision parameters used for ranking of sites for a power plant installation using the soft computing techniques leads to complex formulations that are computationally expensive[41]. Amongst a large number of decision parameters, if some of the parameters do not significantly contribute towards the ranking process, then we need not consider these for decision making. Moreover, it is very tedious to form fuzzy sets for all the 87 decision parameters from several environmental experts, which serve as inputs to certain soft computing techniques used for ranking. The decision parameters comprise of some parameters used to describe air quality, water quality, land suitability, socioeconomic and ecological suitability. We have made an attempt to reduce the number of input decision parameters so that the processing is computationally fast without significantly degrading the accuracy of the end results. We have also attempted to predict futuristic values of some of the relevant parameters to infer site suitability and/or ranking, futuristically (subsequent five years) which can act as a planning tool.

scholarly journals Pork Production Survey to Assess Factors of Facility Design and Operation

2020 ◽  
Vol 12 (11) ◽  
pp. 4536
Author(s):  
Joseph R. Sanford ◽  
Horacio A. Aguirre-Villegas ◽  
Rebecca A. Larson
Keyword(s):  
Water Quality ◽  
Mechanical Ventilation ◽  
Air Quality ◽  
Water Conservation ◽  
Nutrient Management ◽  
Control Strategies ◽  
Facility Design ◽  
Wide Range ◽  
Quality Water ◽  
Negative Impacts

Pork producers can have difficulty operating or expanding existing facilities or establishing new facilities based on perceived negative impacts to the environment and surrounding community. It is critical to understand the characteristics and practices adopted in swine facilities to evaluate the extend of these impacts. A survey, completed by 69 pork producers in Wisconsin, was conducted to assess how facility design and management affect odor, water quality, water consumption, air quality, traffic, and noise. A wide range of production facilities participated in the survey where 29% of respondents were classified as very small (<35 animal units, AU), 16% as small (35–70 AU), 20% as medium (70–300 AU), 23% as large (300–1000 AU), and 12% as permitted (>1000 AU) facilities. Generally, facilities integrated numerous odor control strategies which resulted in high calculated odor scores and the absence of odor complaints. However, the lack of nutrient management planning and other practices for water quality, particularly for facilities with less than 300 AU, indicates there are areas that need improvement. Regardless of facility size, water reduction practices were very commonly reported indicating water conservation is important. Pit ventilation and mechanical ventilation was reported at 58 and 85% of the surveyed facilities, which highlights the need to increase the adoption of mechanical ventilation for air quality, especially in farms with under-barn storage. Using trucks instead of tractors and pumping instead of trucks and tractors can reduce traffic around facilities during manure hauling season.

scholarly journals Towards a cyber-physical era: soft computing framework based multi-sensor array for water quality monitoring

2018 ◽  
Vol 11 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Jyotirmoy Bhardwaj ◽  
Karunesh K. Gupta ◽  
Rajiv Gupta
Keyword(s):  
Water Quality ◽  
Decision Making ◽  
Soft Computing ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Computing Framework ◽  
Data Matrices

Abstract. New concepts and techniques are replacing traditional methods of water quality parameter measurement systems. This paper introduces a cyber-physical system (CPS) approach for water quality assessment in a distribution network. Cyber-physical systems with embedded sensors, processors and actuators can be designed to sense and interact with the water environment. The proposed CPS is comprised of sensing framework integrated with five different water quality parameter sensor nodes and soft computing framework for computational modelling. Soft computing framework utilizes the applications of Python for user interface and fuzzy sciences for decision making. Introduction of multiple sensors in a water distribution network generates a huge number of data matrices, which are sometimes highly complex, difficult to understand and convoluted for effective decision making. Therefore, the proposed system framework also intends to simplify the complexity of obtained sensor data matrices and to support decision making for water engineers through a soft computing framework. The target of this proposed research is to provide a simple and efficient method to identify and detect presence of contamination in a water distribution network using applications of CPS.

scholarly journals Application of soft computing techniques in WWER nuclear power plant safety

2019 ◽  
Vol 5 (2) ◽  
pp. 177-182
Author(s):  
Akbar Abbasi ◽  
Fahreddin Sadikoglu
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Soft Computing ◽  
Nuclear Power ◽  
Power Plants ◽  
Electricity Production ◽  
Plant Safety ◽  
Soft Computing Techniques ◽  
The World ◽  
Nuclear Power Plant Safety

Nowadays, Nuclear Power Plant (NPP) is one of the intended energy resources for the world requirement energy in future, and nuclear power plants provided 11 percent of the world’s electricity production in 2014. Meanwhile, nuclear power plant safety has always been one of the most critical issues in the world. In this paper, the nuclear power plant safety improvement using Soft Computing Techniques were analyzed. For this purpose, the support system based on Neuro-Fuzzy Diagnosis System (NFDs) method and Genetic Algorithms (GAs) approach were used. The obtained result showed that the first symptom is P3 (pressurizer pressure) and second order symptom is P2 (core coolant average temperature) in both approaches. The comparison between the NFDs method and the GAs approaches indicated that the GAs in data test results was faster than the NFDs results.

scholarly journals Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

2020 ◽  
Vol 12 (10) ◽  
pp. 4045
Author(s):  
Muhammad Muhitur Rahman ◽  
Md Shafiullah ◽  
Syed Masiur Rahman ◽  
Abu Nasser Khondaker ◽  
Abduljamiu Amao ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Air Quality ◽  
Soft Computing ◽  
Air Quality Modeling ◽  
Atmospheric Environment ◽  
Quality Data ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Quality Modeling ◽  
Computing Models

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

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