Cost Estimation Research Articles

Cost prediction for water reuse equipment using interpretable machine learning models

During the initial stages of engineering projects management, the accurate estimation of equipment costs plays a crucial role in determining project approval decisions. However, there is a significant research gap regarding the estimation of investment costs for water reuse equipment. Additionally, cost predictions for such construction projects often suffer from limitations, including low accuracy, limited generalizability, and inefficiency. Advanced machine learning (ML) methods, renowned for their ability to model complex decision-making processes, offer powerful solutions. In this study, four traditional models and four ensemble models were employed to predict the cost of water reuse equipment. The results demonstrated that the ensemble models exhibited significantly superior predictive performance to that of traditional models with the three boosting ensemble models achieving the best performance (traditional models: training R2 = 29.77 %–94.85 %, testing R2 = 30.62 %–71.72 %; boosting ensemble models: training R2 = 97.42 %, testing R2 = 82.16 %–93.79 %). Furthermore, this study simplified the features of predictive models and identified the key variables that influence the cost of water reuse equipment using Shapley additive explanations (SHAP) method. The retrained ensemble models re-constructed based on the selected variables achieved significant predictive performance, with the Gradient Boosting Decision Tree (GBDT) outperforming the other models (training set R2 = 97.37 %, testing set R2 = 93.86 %). The water quantity, inflow conductivity, outflow conductivity, and recovery rate emerged as critical factors affecting the cost of water reuse equipment. Overall, the methods proposed in this study can enhance the versatility of cost prediction processes in environmental engineering scenarios, particularly those concerning the construction costs of water treatment equipment.

Strategies of Public University Building Maintenance—A Literature Survey

In this article, comprehensive insights into the field of building maintenance, emphasizing the importance of keywords, collaborative efforts among authors, and the evolving research landscape, are provided. The use stage, as the longest phase in a building’s life cycle, involves economic, technical, and social activities. Numerous authors have contributed to the broader topic of building maintenance, covering aspects such as maintenance prioritization, improvement actions, decision-making guidelines, factors affecting success, cost estimation models, and BIM usage in maintenance. The study conducts a thorough literature review using Scopus as a search engine, employing the full-counting method for authorship, and uses VOSviewer 1.6.20 software for bibliometric analysis to identify gaps and outline future research directions. The results reveal distinct data sets focusing on building maintenance from general to specific, with each set reflecting unique aspects and considerations. Bibliometric maps illustrate author collaboration and keyword connections within each set, highlighting evolving research trends and areas of focus. Overall, the analysis highlights the importance of prioritizing maintenance, integrating technology, and fostering collaboration to enhance building maintenance in educational institutions. The study concludes with recommendations for future research, emphasizing the ongoing need for exploration and development in building maintenance, especially on maintenance strategies and Life Cycle Costs of university buildings.

Reassessment of the Dimensions and Layout of Roadside Canals

Until now, there have been puddles on several sections of the Grand Depok City Boulevard road, which are located in the Sukmajaya and Cilodong Districts. This study aims to review the drainage system at three inundation locations (A, B, and C), which includes layout, runoff discharge, channel design discharge, channel dimensions, culverts, and cost estimation. The results showed that the drainage system was built inadequately, so an outlet channel was needed at location A and culverts at locations A and C. The calculated runoff discharge ranged from 0.16-0.73 m3/second, with a planned channel discharge ranging from 0.38-1.10 m3/sec. With this discharge value, it is necessary to deepen the canal at location A, which was originally 0.5 m to 0.7 m, and to reduce the slope of the canal bottom at location B, which was originally 3% to 2%. The culverts required at location A are 18.5 m long and 0.6 m in diameter, and there are 39 at location C, each 8 m long and 0.2 m in diameter. The estimated total cost is IDR 155,492,000. Construction of canals and culverts should use precast concrete materials and be done at night. Keywords: Reviewing, Roadside channel, Runoff.

Milestones in Surgical Complication Reporting – Twenty years of Clavien-Dindo Classification & ten years of Comprehensive Complication Index (CCI®)

Abstract Background Standardized outcome reporting is key for proper assessment of surgical procedures. A recent consensus conference recommended the Clavien-Dindo classification (CDC) and the Comprehensive Complication Index (CCI®) for assessing postoperative morbidity. However, their use in randomized controlled trials (RCTs) has not been assessed, and several challenging scenarios for grading complications require consensus-based guidance. Aims The aim of this study was to assess the use of the CDC and CCI® in RCTs and to provide guidance on their standardized and consistent application. Methods We identified all RCTs that used the CDC or CCI® as a primary or secondary outcome. In addition, we asked 163 international surgeons to independently grade complications of 20 clinical cases covering seven challenging scenarios. Finally, a core group of five experts used this information do develop consistent recommendations. Results Up to July 2023, 1424 RCTs used the CDC or CCI® to assess postoperative morbidity. Annual use was steadily increasing with now over 200 new RCTs per year. Eighty-nine (55%) surgeons completed the survey. Complications requiring multiple interventions, complications of complications, complications occurring prior to referral, and expected and unrelated complications should all be counted as separate complications and included in the CCI®. Invasive diagnostics without findings should not be considered as a complication since purely diagnostic. Conclusion We observed an extensive and steadily increasing use of CDC and CCI® in RCTs, highlighting the importance of their consistent application. Provided by the original developers of the CDC and CCI® and based on an international survey of their frequent users, the current consensus offers much-needed guidance for challenging scenarios. This will further improve the consistency and accuracy of complication reporting, leading to higher quality RCTs, improved cost estimations, and better quality control, ultimately benefiting all stakeholders.

Why modification of the ECLAC methodology for Bangladesh is important? Observation from housing, commerce and transport sector in cyclone Mora in Teknaf Upazila, Cox's Bazar, Bangladesh

An immediate and precise Damage and Loss Assessment (DaLA) is necessary after the occurrence of any disaster to bounce back to its previous stage perceiving the recovery needs. Since the ECLAC (Economic Commission for Latin America and the Caribbean) methodology has been identified as the most consistent and comprehensive one worldwide for DaLA, this research aims to apply and modify the ECLAC for the housing, commerce, and transportation sectors in the context of Bangladesh as it needs to incorporate both its formal and informal economies in DaLA. An immediate field visit was performed in Teknaf Upazila after the occurrence of cyclone Mora in 2017 following 12 Key Informant Interviews (KIIs) and 135 individual surveys for the above mentioned sectors. This study considers alternative sources for baseline data because of its unavailability and a constant Likert scale throughout the assessment. While applying the ECLAC, several adjustments have been obtained depending on the local characteristics that create differences in the estimation of modified DaLA from the conventional ECLAC. In the case of the housing sector, all three damage, loss, and additional cost estimations are modified. On the other hand, the damage and especially the loss have been revised for the commerce sector. Also the loss and additional cost estimation for the transportation sector have been separated and modifications have been adopted. This study may help to estimate avoiding the over and under-estimation of damage and loss and can be applied in future disasters in Bangladesh and other developing countries. The researchers can develop a complete ECLAC methodology examining the other sectors and the government can take initiatives to apply the revised ECLAC methodology for a proper reconstruction and recovery planning.

Decarbonising LNG value chain using carbon capture and storage: why, where, how much and what are the limitations?

As a result of global aspirations to achieve energy transition along with the desire to ensure energy security, energy exporters proposed a series of new ‘carbon-neutral’ products – liquefied natural gas (LNG) and oil. In 2019, a new market was officially born after the first-in-history shipment of carbon-neutral LNG cargo from Shell to Japanese Tokyo Gas. Until 2021, it grew rapidly, however in 2022, announcements about volumes delivered to the customers significantly dropped. In this paper, we will explore the reasons for this dynamic, review perspectives of the carbon-neutral fuels markets, and explore the potential steps that producers and consumers could undertake to achieve global climate targets. We specifically focus on the consequences of implementing carbon capture and storage (CCS) technologies. Based on the data on the world CCS facilities, provided by the International Energy Agency, we conduct preliminary analysis of the capital costs associated with the construction of carbon capture, utilisation, and storage facilities. After that, we apply the cost estimation method (class 5) based on the US Department of Energy Classification, to estimate the costs of using CCS for emissions management in different regions/countries. As a result, the study demonstrates that due to the various economic, geographical and infrastructural limitations, it is more feasible to implement CCS on the producer’s side than on the consumer’s side. We also show what level of CCS implementation within the LNG value chain is economically feasible for different market participants and what the costs are.

Estimating Water Supply Requirement on Pahang Soil Series to Achieve a Minimal Water Consumption for Resource Efficiency in Agroecology

Agroecology has ten elements that can be implement in the agricultural activities and food systems to achieve a more sustainable future in producing and consuming foods. Water saving is one of the important attributes of agroecological practices. In this study, we are investigating soil series information provided by the Department of Agriculture, Malaysia. There are 34 soil series identified in Pahang state. The existing information provided with the soil series do not guide the implementation of irrigation management in terms of water consumption requirement for plantation land management. The current study converts soil series information into soil textures. There are seven soil textures determined in Pahang states. They were Clay, Silty Clay, Clay Loam, Silty Clay Loam, Sandy Clay Loam, Loam, and Sand. Soil textures were used to estimate soil hydraulic properties to represent the soil series. Soil field capacity, permanent wilting point, and plant available water were determined. Water infiltration simulation was carried out on all soil textures, and it was found silty clay loam (Gugut Series, Setol Series) was one of the best soil textures in terms of water infiltration time, and water retention in the soil. The information derived from the current study can be used for land management planning to estimate the irrigation water supply needed, which will be useful for cost estimation and water resource management.

Effect of Procurement Planning on Organizational Performance Within the Public Sector: A Case of Ruhengeri Referral Hospital in Musanze District, Rwanda

Public entities in Rwanda spend a significant amount of money on procurement. This is the process of acquiring goods, works, or services from a vendor. The general objective of this study was to assess the effect of procurement planning on organizational performance within the public sector. Specifically, the study assessed the effect of needs assessments in procurement planning on performance of Ruhengeri referral hospital, analyze the effect of quality specification in procurement planning on performance of Ruhengeri referral hospital, examine the effect of cost estimation in procurement planning on performance of Ruhengeri referral hospital and assess the effect of terms of payment in procurement planning on performance of Ruhengeri referral hospital. Research was guided by Resource Based viewpoint theory and Transaction Cost Economics theory. Descriptive research design was adopted in this research. This study has taken 106 populations who have direct works related to procurement planning functions from Ruhengeri referral hospital and reporting health centers (42 working at Ruhengeri referral hospital main location and 64 from 12 health centers under management of Ruhengeri referral hospital). The researcher has determined that due to the small population size, a census inquiry method employed instead of sampling. This study used both primary and secondary data, and each has its specific instruments for collection including questionnaire, documentary and interview. Here the researcher tends to interpret frequency, percentages, mean, and standard deviation, as results of Statistical Package for Social Scientists (SPSS) version 20 while content analysis used to analyze qualitative data. The Model Summary provides valuable observations into the effect of independent variables Terms of payment in procurement planning, Needs identification in procurement planning, Quality specification in procurement planning, Cost estimation in procurement planning on the dependent variable, organization performance. The findings revealed the coefficient of determination (R Square), which is 0.652, demonstrates that 65.2% of the variance in organizational performance of Ruhengeri Referral Hospital explained Terms of payment in procurement planning, Needs identification in procurement planning, Quality specification in procurement planning, Cost estimation in procurement planning. The results indicate the coefficient of needs identification in procurement planning (β: 0.399, p: 0.000), it shows the significant effect of needs assessments in procurement planning on organizational performance of Ruhengeri referral hospital. The results indicate the coefficient of quality specification in procurement planning (β: 0.387, p: 0.001), it shows the significant effect of quality specification in procurement planning on organizational performance of Ruhengeri referral hospital. The results indicate the coefficient of cost estimation in procurement planning (β: 0.152, p: 0.022), it shows the significant effect of cost estimation in procurement planning on organizational performance of Ruhengeri referral hospital. The results indicate the coefficient of terms of payment in procurement planning (β: 0.237, p: 0.021), it shows the significant effect of terms of payment in procurement planning on organizational performance of Ruhengeri referral hospital. It is recommended that Ruhengeri Referral Hospital continues to strengthen its needs assessment process by involving a wider range of stakeholders.

Гипотеза об абсолютной ценности ресурсов недр

Practical assessment of deposits and resources in the Kyrgyz Republic is based on the cost estimation, income and/ or cost methods. The aggregated economic indicators of reduced net cash flows and the internal rate of return, in the dominant sense, reflect the work of capital and premium on capital without legal and economic categories of the mining rent by types of payment for ownership of resources. Currently, the cost of a mining project is defined by the norms of imposed taxes and payments, and, to a large extent, by experimental innovations in the form of a share in the distribution of the net cash flows, bidding and auction payments. Based on the study of the Kumtor mining project for the period of 1996–2020, limits of the mining rent are defined by type, the influence of secondary factors of the economic return from mining and the factor of the project's influence on the macroeconomic indicators of the country. The integrated indicators of the three factors are combined into the category of resource value with the boundaries of revenue sharing for the subsoil owner and the investment capital.

Estimation of Cost and Income of Onion Production: A Case Study of Ballia District of Eastern Uttar Pradesh, India

The system of India’s economy predominantly depends on agriculture. It is contains attractive good qualities of calories. The present research study on entitled “Estimation of Cost and Income of Onion Production: A Case Study of Ballia District of Eastern Uttar Pradesh This research article focused on the following specific objectives are (1) To work out the cropping intensity, costs-return and input-output relationship of onion cultivation in the district and second objectives (2) To find out the constraints in production of onion confronted by Farmers. Multistage stages sampling technique were used for selection of respondent. Primary data was collected by personal interview methods with the use of pre structured and pre listed schedule questionnaire. Finally 100 farmers were selected randomly from four selected villages. The data were related to the Rabi season in the agriculture cropping year 2021-22. Overall average costs of cultivation of respondent were found per hectare to be Rs. 111493.95. The higher costs of cultivation were observed on medium farms to Rs.118430.40 fallowed by small farms and marginal as Rs.109070.72 and Rs.106980.72 per hectare respectively. Per quintal cost of production and ratio of cost of Input –output analysis were calculated cost A1 toC1, C2 and C3 basis.

Developing preliminary cost estimates for foundation systems of high-rise buildings

This study provided a unique Artificial intelligence (AI) model for the cost estimation of the foundation system for high-rise buildings. First, parametric data research was conducted, including 180 examples of foundation systems for high-rise building models related to criteria such as average column spacing, floor height, slap load, overall weight, base shear, and over. In addition, moment, gravity load, seismic load, design load, various columns (mid-edge-corner), soil profile, bearing capacity, GWT, excavation depth, and foundation systems are all factors to consider (shallow and deep footing). The research encompassed column spacing (5.0, 7.0, and 9.0 m) and the number of stories from 15 to 35, with an average cost decrease of 15%. The AI model utilizes extensive machine learning algorithms to analyze structural and geotechnical parameters. It provides a predictive tool that improves decision-making in the preliminary design. The methodology includes collecting data, training models utilizing innovative neural network techniques and validating them against current cost estimation models. The findings indicate that the AI model significantly improves the accuracy of cost estimations, with a precision rate of 95%, compared to traditional methods. Moreover, the model presents a potential cost savings of up to 15%. Construction designers and decision-makers may utilize the study’s findings to increase the accuracy of foundation system selection, lower costs and construction length, and improve overall project performance. Future work may involve applying this concept to infrastructure and substructure projects and collecting more data for benchmarking and empirical study.

Critical Considerations in Power Measurements for the Precise Estimation of Energy Costs in Plasma NOx Synthesis

Critical Considerations in Power Measurements for the Precise Estimation of Energy Costs in Plasma NOx Synthesis

Estimating growout production costs of commercial‐scale marine finfish production in southern tier US states

AbstractIncreased interest in marine fish farming in the United States has led to a need for fundamental economic information on production of candidate species for commercialization in various production systems. Funding for the project targeted those species with potential for production in southern tier states in the United States. Sufficient technical data were found to develop comprehensive budget analyses to estimate growout production costs for four scales of production for each of 10 species with potential for production in ponds, 13 in recirculating aquaculture systems (RAS), and five in net pens. The choice of species/production system scenarios was based on evidence of successful production on farms or in research. Estimation of production costs with enterprise budgets can provide useful guidance to identify the types of improved efficiencies that have the greatest effect on economic viability even when commercial farm data are not available. Commercial farm data were used where available, but for most species, data were available only from studies conducted under research conditions. Per‐kg costs of production were lowest for net pen production, followed by ponds, with production costs in RAS two to five times greater than in ponds or net pens. Ponds and net pens generally exhibited greater efficiency of use of capital assets across species than did RAS that resulted in lower percentages of fixed costs and lower annual costs per kg of fish produced. All five species evaluated for net pen production were estimated to be profitable, including redfish, Sciaenops ocellatus (also known as red drum), striped bass, Morone saxatilis, cobia, Rachycentron canadum, red snapper, Lutjanus campechanus, and seriolids (generic budget for almaco jack, Seriola rivoliana, California yellowtail, Seriola lalandi, and greater amberjack, Seriola dumerili), four in ponds (redfish, hybrid drum, ♀Pogonias cromis × ♂Sciaenops ocellatus, black sea bass, Centropristis striata, and cobia), but none of the RAS scenarios showed profitability at average yields (kg/cubic meter) reported in the literature. Comprehensive data on growout production of marine finfish species in the United States is generally lacking, and there is a strong need for production trials conducted under near‐commercial conditions with an endpoint of market‐sized fish. Production trials should be conducted in ponds (≥0.1 ha), net pens, and RAS tanks of a size that simulate commercial production conditions. Adequate production trial databases would provide opportunities to develop economic optimization models that would provide useful guidance for prospective producers. Average yields (kg/cubic meter) in RAS will need to be much greater than currently reported in the research literature for RAS production to be economically sustainable. Net pen production appears to be profitable in the United States, but effective permitting procedures are not in place. Providing research support for the US redfish sector that has developed effective production and marketing models could serve as a foundation for developing additional species and offer opportunities for diversification on marine finfish farms.

Learning complex predicates for cardinality estimation using recursive neural networks

Cardinality estimation is one of the most vital components in the query optimizer, which has been extensively studied recently. On one hand, traditional cardinality estimators, such as histograms and sampling methods, struggle to capture the correlations between multiple tables. On the other hand, current learning-based methods still suffer from the feature extraction of complex predicates and join relations, which will lead to inaccurate cost estimation, eventually a sub-optimal execution plan. To address these challenges, we present a novel end-to-end architecture leveraging deep learning to provide high-quality cardinality estimation. We exploit an effective feature extraction technique, which can fully make use of the structure of tables, join conditions and predicates. Besides, we use sampling-based technique to construct sample bitmaps for the tables and join conditions respectively. We also utilize the characteristics of predicate tree combined with recursive neural network to extract deep-level features of complex predicates. Finally, we embed these feature vectors into the model, which consists of three components: a recursive neural network, a graph convolutional neural network (GCN) and a multi-set convolutional neural network, to obtain the estimated cardinality. Extensive results conducted on real-world workloads demonstrate that our approach can achieve significant improvement in accuracy and be extended to queries with complex semantics.

A Hybrid Artificial Bee Colony and Artificial Fish Swarm Algorithms for Software Cost Estimation

Software cost estimation (SCE), estimating the cost and time required for software development, plays a highly significant role in managing software projects. A somewhat accurate SCE is necessary for a software project to be successful. It allows effective control of construction time and cost. In the past few decades, various models have been presented to evaluate software projects, including mathematical models and machine learning algorithms. In this paper, a new model based on the hybrid of the artificial fish swarm algorithm (AFSA) and the artificial bee colony (ABC) algorithm is presented for SCE. The initial population of AFSA, which includes the values of the effort factors, is generated using the ABC algorithm. ABC algorithm is used to solve the problems of the AFSA algorithm such as population diversity and getting stuck in a local optimum. ABC algorithm achieves the best solutions using observer and scout bees. The evaluation of the combined method has been implemented on eight different data sets and evaluated based on eight different criteria such as mean magnitude of relative error and PRED (0.25). The proposed method is more error-free than current SCE methods, according to the results. The error value of the proposed method is lower on NASA60, NASA63, and NASA93 datasets.

Consecutive high-performance removal of Cu2+ metal ions and Deltamethrin using multifunctional pyrolysis cuttlebone/cotton fabric nanocomposite

A simple technique was developed for the modification of cotton materials that is inexpensive, environmentally friendly, and very effective. Waste Cotton fabrics (WCFs) are loaded with propolis extract (PE) for Cu2+ removal. Then, Cu2+ underwent a pyrolysis process with modified cuttlebone (CB) at 900 °C for 5 h. The surface of the prepared materials was characterized using X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray (SEM-EDX), Fourier transform infrared (FTIR), BET, particle sizes, thermogravimetric analysis (TGA) and zeta potential analysis. The Cu2+ metal ions from an aqueous solution were removed using WCFs/PE, and DLM was subsequently removed using pyro WCFs/PE/Cu/CB. The as-prepared NPs exhibited the face-centered cubic structure of WCFs/PE/Cu/CB with crystallite sizes ranging from 386.70 to 653.10 nm. FTIR spectra revealed that CB was present on the surface of the resulting WCFs/PE/Cu. SEM revealed the dispersion of a uniformly flower-like morphology over a large area. Sorption studies were performed based on parameters that included pH, dose, contact time, and initial concentration. The adsorption isotherm and the kinetic studies of the DLM adsorption process were applied at a pH of 5.0 and a temperature of 25 °C using several isotherms and kinetic models. The results revealed qmax (20.51 mg/g) with R2 = 0.97, the Langmuir isotherm that best matches the experimental data. Hence, the Langmuir isotherm suggests that it is the model that best describes sorption on homogenous surfaces or surface-supporting sites with various affinities. The correlation coefficient R2, χ2, adjusted correlation coefficient, and error functions like root mean square (RMSE), normalized root mean square error (NRMES), and mean absolute error (MAE) were used to evaluate the best-fit models to the experimental adsorption data. Moreover, cost estimation for the prepared adsorbent WCFs/PE/Cu showed that it costs approximately 3 USD/g, which is a cheap adsorbent compared to other similar adsorbents reported in the literature. The examined WCFs/PE have significant applicability potential for Cu2+-laden wastewater treatment due to their superior Cu2+ metal ions adsorption capability and reusability.The cytotoxicity and safety study showed that at higher concentrations, it resulted in much less cell viability. Additionally, the removal efficiency of Cu2+ metal ions from synthetic, realistic industrial wastewater using WCFs/PE reached up to 96.29 %, demonstrating good adsorption capability. Thus, there is a huge possibility of accomplishing this and performing well. This study paves the way for the reuse and valorization of selected adsorbents following circular economy principles.Two green metrics were applied, the Analytical Eco-scale and the Analytical GREEnness Calculator (AGREE).

Quantitative Analysis of Green H2 Production Costs: A Comparison between Domestic Developed and Imported Electrolyzers

This study aims to present a quantitative cost analysis of hydrogen utilizing a developed alkaline electrolyzer, a similar-capacity imported alkaline electrolyzer, and a similar-capacity PEM electrolyzer.The research also finds the key parameters that can reduce or increase the production cost. One of the subjected electrolyzers is a locally developed Alkaline Electrolyzer (AE); the other two are similar-capacity imported AE and Polymer Electrolytic Membrane (PEM) electrolyzers. The study uses the Hybrid Optimization of Multiple Energy Resources (HOMER) software for estimating the Levelized Cost of Electricity (LCOE) and the Life Cycle Cost (LCC) method for hydrogen production cost estimation. Results show that the imported electrolyzers have higher production costs due to import duty, fees, and taxes. The estimated cost is 88.4% (AE) and 110.3% (PEM), higher than the locally developed electrolyzer. The economic changes also significantly impact production costs. Government policies can reduce the cost by rescheduling the hydrogen components taxes.

Polymer-grade propylene production through temperature swing adsorption: Molecular simulation and techno-economic analysis

Producing polymer-grade propylene from the propylene-rich mixtures can be quite challenging since the most commonly employed technology, distillation, requires high-energy input and is expensive. Therefore, identifying less-costly, efficient, and environmentally friendly methods, such as the use of the adsorptive process, is highly desirable. In this regard, six pure silica zeolites (AFV, AST, AVL, IFR, LEV, and SWY) as propane selective adsorbents are investigated for the separation of 10 mol.% propane and 90 mol.% propylene mixture. Molecular simulation Monte Carlo (MC) is used to calculate framework structure parameters such as pore volume, surface area, and framework density of pure silica zeolites. Also, adsorption isotherms, propane selectivity, and working capacity are presented using MC calculations. The Langmuir-Freundlich isotherm model is utilized to calculate isotherm parameters. A system model is implemented in Aspen Adsorption software (version 11) and breakthrough curves are achieved. The Temperature Swing Adsorption (TSA) cycle is simulated and key performance parameters such as propylene purity, recovery, and productivity for all six silica zeolites are calculated. Finally, economic analysis of the TSA process is performed using the Aspen Capital Cost Estimator (version 11) and Aspen Process Economic Analyzer (version 11) and compared with the cost of the conventional distillation process. Results showed that the capital cost and operating cost of temperature swing adsorption are 42.3 % and 91.37 % less than those of distillation, respectively.

Identification of Beach Erosion in Colombo- North Coast Line: Effectiveness of the Engineered Solutions

Coastal erosion is a silent disaster in Sri Lanka due to the loss of valuable land. Already, millions of rupees have been spent on coastal protection. This study assessed the long-term effectiveness of those engineered solutions, especially focusing on the Colombo- North coastline. The effectiveness was assessed by comparing erosion rates in the last 20 years with available historical data. Shorelines of the last 20 years were observed and digitized by Google Earth image analysis and erosion rates were calculated by GIS and Digital Shoreline Analysis (DSAS) tools. The evolution of the structures was observed and analysed by satellite image analysis and other available data from reliable sources such as reports produced by the Government. Cost estimation for the observed structures was carried out using BOQ analysis and discounted methods. Results showed that more than 60 numbers of structures have been constructed during the last 20-30 years along the study area. Uswetakeyyawa to Estuary of Kelani River stretch was identified as the region with the highest structure density. When analyzing the results, it was revealed that the structures built in that region have reduced the erosion rate (0.43 m/year) compared to the 1996 rates (2.5 m/year). Digital shoreline analysis further indicated effectiveness as most of the protected areas from Negombo to Maha Oya estuary have been accreted during the last 20 years while unprotected areas from Uswetakeyyawa to Negombo lagoon, have been subjected to erosion. According to cost estimations, approximately Rs 1.23 billion have been spent in the last 20-30 years on the entire study area. Further, the cost analysis of the Uswetakeyyawa to Kelaniya stretch revealed that the investment for 1m accretion was Rs 28.5 million. However, the cost-effectiveness of the structures should be further assessed against the land loss value with an integrated Coastal Vulnerability Index.

Smart cost estimation: Empirical case for extra-high voltage transmission towers

Nowadays, electricity has become an integral part of human lives. Most of our daily appliances, tools, and personal belongings are inseparable from electricity. To ensure a proper electricity distribution with an efficient transfer capability, Extra-High Voltage (EHV) transmission towers are needed. To design such a structure, it is of utmost importance to account for the cost of said tower. However, the process to estimate the cost of EHV transmission towers is both time-consuming and strenuous on human labor since a lot of consideration have to be taken. To overcome this, an imperative requirement exists for a prompt, precise, and automated tool to replace the existing manual cost estimation method. This research endeavor aims to craft a tool using support vector regression (SVR) with the capacity to prognosticate construction expenses for projects involving EHV transmission towers. The exploration of pertinent literature has enabled us to amass historical data and delineate the attributes essential for estimating costs linked to EHV transmission tower construction. The investigation delves into a comprehensive dataset spanning the past decade in Taiwan. Within this timeframe, 317 EHV transmission towers were erected between 2009 and 2019. However, 79 of these instances are excluded due to incomplete information, thereby yielding 238 viable datasets (comprising 75 % of the overall total) to underpin the development of the SVR model. By configuring the parameters to C = 0.2 and γ = 0.1, followed by 5-fold cross-validation, the resultant SVR model attains a remarkable prediction accuracy of 97.91 %, on average. As a result, the proposed SVR-based model can effectively and accurately predict the cost of constructing an EHV transmission tower project and reduce the time spent on estimation, thus contributing to the enhancement of the resilience and robustness of the transmission network system.