scholarly journals Back Cover: Early-Stage Capital Cost Estimation of Biorefinery Processes: A Comparative Study of Heuristic Techniques (ChemSusChem 17/2016)

ChemSusChem ◽  
2016 ◽  
Vol 9 (17) ◽  
pp. 2515-2515
Author(s):  
Mirela Tsagkari ◽  
Jean-Luc Couturier ◽  
Antonis Kokossis ◽  
Jean-Luc Dubois
Keyword(s):  
Comparative Study ◽  
Cost Estimation ◽  
Early Stage ◽  
Capital Cost ◽  
Back Cover ◽  
Heuristic Techniques

scholarly journals Early-Stage Capital Cost Estimation of Biorefinery Processes: A Comparative Study of Heuristic Techniques

ChemSusChem ◽  
2016 ◽  
Vol 9 (17) ◽  
pp. 2284-2297 ◽  
Author(s):  
Mirela Tsagkari ◽  
Jean-Luc Couturier ◽  
Antonis Kokossis ◽  
Jean-Luc Dubois
Keyword(s):  
Comparative Study ◽  
Cost Estimation ◽  
Early Stage ◽  
Capital Cost ◽  
Heuristic Techniques

scholarly journals Uncertainties in Early-Stage Capital Cost Estimation of Process Design – A Case Study on Biorefinery Design

2015 ◽  
Vol 3 ◽  
Author(s):  
Peam Cheali ◽  
Krist V. Gernaey ◽  
Gürkan Sin
Keyword(s):  
Process Design ◽  
Cost Estimation ◽  
Early Stage ◽  
Capital Cost

ASSESSMENT OF KIDNEY FUNCTIONAL RESERVE ON THE BACKGROUND OF ANTI-AGGREGATE THERAPY IN CHRONIC KIDNEY DISEASE II-III STAGE

2020 ◽  
Vol 6 (1) ◽  
pp. 49-54
Author(s):  
Khabib Barnoev ◽  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Comparative Study ◽  
Early Stage ◽  
Raw Material ◽  
Functional Reserve ◽  
Functional Reserves ◽  
Term Administration ◽  
Antiaggregant Therapy

The article presents the results of a study to assess the functional reserve of the kidneys against the background of a comparative study of antiaggregant therapy dipyridamole and allthrombosepin in 50 patients with a relatively early stage of chronic kidney disease. Studies have shown that long-term administration of allthrombosepin to patients has resulted in better maintenance of kidney functional reserves. Therefore, our research has once again confirmed that diphtheridamol, which is widely used as an antiaggregant drug in chronic kidney disease, does not lag behind the domestic raw material allthrombosepin

scholarly journals Methodology for the Concept Design of Locally Reinforced Composites

2021 ◽  
Vol 11 (16) ◽  
pp. 7246
Author(s):  
Julius Moritz Berges ◽  
Georg Jacobs ◽  
Sebastian Stein ◽  
Jonathan Sprehe
Keyword(s):  
Cost Estimation ◽  
Structural Design ◽  
Early Stage ◽  
Parameter Study ◽  
Great Effort ◽  
Concept Design ◽  
Cost Weight ◽  
Reinforced Plastics ◽  
Fiber Reinforced Plastics ◽  
The Cost

Locally load-optimized fiber-based composites, the so-called tailored textiles (TT), offer the potential to reduce weight and cost compared to conventional fiber-reinforced plastics (FRP). However, the design of TT has a higher complexity compared to FRP. Current approaches, focusing on solving this complexity for multiple objectives (cost, weight, stiffness), require great effort and calculation time, which makes them unsuitable for serial applications. Therefore, in this paper, an approach for the efficient creation of simplified TT concept designs is presented. By combining simplified models for structural design and cost estimation, the most promising concepts, regarding the cost, weight, and stiffness of TT parts, can be identified. By performing a parameter study, the cost, weight, and stiffness optima of a sample part compared to a conventional FRP component can be determined. The cost and weight were reduced by 30% for the same stiffness. Applying this approach at an early stage of product development reduces the initial complexity of the subsequent detailed engineering design, e.g., by applying methods from the state of the art.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

scholarly journals Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yoonseok Shin
Keyword(s):  
Cost Estimation ◽  
Construction Projects ◽  
High Performance ◽  
Learning Algorithm ◽  
Early Stage ◽  
Construction Project ◽  
Regression Tree ◽  
Building Construction ◽  
Regression Problem ◽  
Additional Information

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

Capital Cost Estimation: A Reference Class Approach

2012 ◽  
pp. 135-157
Author(s):  
Mark J. Kaiser ◽  
Brian F. Snyder
Keyword(s):  
Cost Estimation ◽  
Capital Cost ◽  
Reference Class

FUZZY MODELS IN CAPITAL COST ESTIMATION OF CHEMICAL PLANTS

1984 ◽  
pp. 500-506 ◽  
Author(s):  
I. Turunen ◽  
M. Järveläinen ◽  
M. Dohnal
Keyword(s):  
Cost Estimation ◽  
Capital Cost ◽  
Chemical Plants ◽  
Fuzzy Models

scholarly journals An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Meseret Getnet Meharie ◽  
Zachary C. Abiero Gariy ◽  
Raphael Ngumbau Ndisya Mutuku ◽  
Wubshet Jekale Mengesha
Keyword(s):  
Factor Analysis ◽  
Variable Selection ◽  
Cost Estimation ◽  
Construction Projects ◽  
Early Stage ◽  
Selection Process ◽  
Fuzzy Ahp ◽  
Geometric Mean ◽  
Limited Information ◽  
Input Variables

Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide-ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.

Sign in / Sign up

Export Citation Format

Share Document