WASTEWATER TREATMENT PLANT OPERATION COSTS

The aim of this study was to evaluate the energy usage and total operation cost of the tractor grabber. The data was collected at FELCRA BERHAD Kawasan Titi Gantong, Bota, Perak. The total cost included all the operation in loading system and evacuation process of fresh fruit bunch (FFB). This study evaluated the energy usage of tractor grabber for three consecutive months using a newly purchased Mini Tractor Grabber Kubota L4400. This study was conducted based on the energy input-output methodology. Mathematical equations are used to determine the field capacity and examine energy usage. The result found that 153.38 MJha⁻¹ of input energy was required to produce 328.43 metric ton per month of FFB in June. The energy was dominated by fuel consumption, machinery and followed by labour. The total operating cost is based on the field capacity of the machine. This study was conducted and obtained the result, assumed that the total cost for a year is RM 47,444.24 and the cost per metric ton is RM 9.76 with energy consumption of 174.23 MJ. Conclusively, it means that the energy usage and cost of operation is efficiently and effectively used in oil palm productivity in FELCRA BERHAD Kawasan Titi Gantong.

Simulation of air quality and cost to ventilate swine farrowing facilities in winter

Lean holistic fuzzy methodology employing cross-functional worker teams for new product development projects: A real case study from high-tech industry

Optimal design and sensitivity analysis of post-combustion CO2 capture process by chemical absorption with amines

Trichloroethylene (TCE) and other solvents are found as groundwater contaminants in industrial complexes. Pump-andtreat (PAT) systems are commonly selected for remediation, but have the disadvantages of long operation time and high cost. To reduce the remediation time and cost of PAT systems, a cost-effective optimal design was obtained with a simulation–optimization model. MODFLOW and MT3DMS were used for simulating groundwater flow and contaminant transport. A genetic algorithm was used for optimization, and a parallel computing technique was used for faster optimization. Cost-effective optimal design was achieved using two objective functions, which calculated the total remediation cost by means of installation, operational, and maintenance costs. Based on the restrictions in the study area, two remediation scenarios were examined. Scenario 1 involved the removal of TCE, and Scenario 2 involved the containment of TCE within the compliance line. The total remediation costs of scenario 2 were 77.4~90.4% of scenario 1. The remediation time decreased from 9.87 years in scenario 1 to 8.13 years in scenario 2. Sensitivity analysis determined the effects of installation, operational, and maintenance costs on the total remediation cost, showing that optimal total cost and remediation time were most affected by operational cost. A parallel computing technique using 40 networked slave processes increased optimization speed by 16.9 times. However, the efficiency of the parallel computing technique decreased as networking time increased.

"The present investigation was undertaken to study about costs and returns from the cultivation of cauliflower in Sikar district of Rajasthan. In this study, primary data were collected from the selected cauliflower cultivators through personal interview method with the help of pre- structured schedule. The study concluded that, on an average, total costs of cauliflower cultivation was estimated at ` 79786 per hectare. Out of which, overall total operational costs were observed as ` 55718 per hectare (i.e., 69.83 percent of total costs). It was decreased with the increase in farm size holdings whereas, total fixed costs were calculated at ` 24068 per hectare which accounts for 30.17 per cent of total costs. It was found positive relationship with the size of farms holdings. Total cost of cultivation of cauliflower was highest on medium farms (` 91764) and lowest on marginal farms (` 77511). The results also revealed that major components of cost of cauliflower cultivation were found human labour with ` 22482 per hectare followed by rental value of owned land (` 21265 per hectare), cost of manure (` 10340 per hectare) and cost of improved seed (` 9498 per hectare). The results of study indicated that overall gross returns obtained from cauliflower cultivation were ` 243275 per hectare. Gross returns were found to be highest (` 286750 per hectare) on medium farm and lowest (` 231250 per hectare) on marginal farms. On an average, cost of production in cauliflower was estimated at ` 670. The cost of production was found maximum (` 682 per quintal) on marginal farms and minimum (` 651 per quintal) on medium farms. It was decreased with increase in the farm size groups. It was observed that, an overall return per rupee earned from cauliflower cultivation was ` 2.77. Among the farm size groups, return was estimated as ` 2.71, ` 2.82, ` 2.83 and ` 2.84 per rupee on marginal, small, semi-medium and medium farm, respectively."

This paper investigates a fully decentralized model for electricity trading within a transactive energy market. The proposed model presents a peer-to-peer (P2P) trading framework between the clients. The model is incorporated for industrial, commercial, and residential energy hubs to serve their associated demands in a least-cost paradigm. The alternating direction method of multipliers (ADMM) is implemented to address the decentralized power flow in this study. The optimal operation of the energy hubs is modeled as a standard mixed-integer linear programming (MILP) optimization problem. The corresponding decision variables of the energy hubs operation are transferred to the peer-to-peer (P2P) market, and ADMM is applied to ensure the minimum data exchange and address the data privacy issue. Two different scenarios have been studied in this paper to show the effectiveness of the electricity trading model between peers, called integrated and coordinated operation modes. In the integration mode, there is no P2P energy trading while in the coordinated framework, the P2P transactive energy market is taken into account. The proposed model is simulated on the modified IEEE 33-bus distribution network. The obtained results confirm that the coordinated model can efficiently handle the P2P transactive energy trading for different energy hubs, addressing the minimum data exchange issue, and achieving the least-cost operation of the energy hubs in the system. The obtained results show that the total operating cost of the hubs in the coordinated model is lower than that of the integrated model by $590.319, i.e. 11.75 % saving in the costs. In this regard, the contributions of the industrial, commercial, and residential hubs in the total cost using the integrated model are $3441.895, $596.600, and $988.789, respectively. On the other hand, these energy hubs contribute to the total operating cost in the coordinated model by $2932.645, $590.155, and $914.165 respectively. The highest decrease relates to the industrial hub by 14.8 % while the smallest decrease relates to the residential hub by 1 %. Furthermore, the load demand in the integrated and coordinated models is mitigated by 13 % and 17 %, respectively. These results indicate that the presented framework could effectively and significantly reduce the total load demand which in turn leads to reducing the total cost and power losses.

Energy consumption and economic evaluation of water desalination by hydrate phenomenon

Benefits of transmission switching and energy storage in power systems with high renewable energy penetration

System for domestic hot water (DHW) preparation generally represents a considerable portion of the energy and water consumption in hotels worldwide. As a consequence, a vast amount of heat is wasted through the drainage system from rooms and kitchens. Also, for hotel-type buildings, demand for DHW can vary according to their occupancy. Researchers and practitioners have a very challenging task to select the optimal and most sustainable solution. This paper analyses a DHW heating system with a heat pump system that uses grey wastewater (GWW) and rainwater as an energy source. The application is developed in combination with experimental site measurements (real system operation) and required standards. The research deals with the subject system in bivalent operation (basic and advanced configuration of heat pump system assisted with gas boiler). In order to evaluate the application of the advanced heat pump DHW heating system with GWW source, a comprehensive assessment is done with a gas boiler system only (base case) and solar plate collector system. For the advanced heat pump system, the study has determined the two key operation elements: hotel occupancy rate and system regulation. The financial assessment showed very promising economic metrics (PBP = 6.54 years, POP = 7.9 years IRR = 9.40%, MIRR = 17.00%) compared to the base case. The study provided results of a sensitive cost-effective system assessment for periods with lower than average hotel occupancy rates. Also, when the total operation and investment costs are compared with the data calculated on an average annual hotel occupancy rate of 68%, the final conclusion is that the investment in the advanced heat pump system is justified only if its gas consumption share is less than 14.27%. The analysis confirmed the main advantages of this heat pump system which are lowered energy consumption and reduced total operating costs.

(ProQuest: ... denotes formulae omitted.)1 Research Premises and HypothesisMaintenance costs have a consistent weight in the total costs generated by an application during its entire lifecycle. Previously conducted research and studies that focus on software maintenance support this claim. In [2], the 60/60 rule of software is depicted. The rule claims that maintenance accounts for 60% of the total costs generated by a software in its entire lifecycle. Enhancement or perfective [3] maintenance tasks account for 60% of all the costs generated by maintenance [2], whereas error correction or corrective [3] maintenance tasks account only for 17% of total maintenance costs [2]. In [4] it's stated that software applications evolve over time to meet the changing needs which leads to maintenance generating 67% of the applications total lifecycle costs. In [5] it is argued that maintenance typically accounts for 75% or more of the total software workload. Other relevant research place maintenance costs at 90% [6], 75% [7], 90% [8] or at 60% to 70% [9] of the overall application's lifecycle cost. It is therefore of paramount importance to accurately estimate maintenance cost before implementing a software project in order to better comprehend the total costs associated with developing and owing a software application. Better understanding maintenance costs will provide valuable information regarding resource planning and the appropriate time to decommission the application.The cost of maintenance is directly determined by the number of people involved in the maintenance process and the number of hours each person invests in the maintenance tasks. Thus in order to estimate maintenance cost one needs to estimate the effort invested in the maintenance process. The maintenance effort is expressed in man-hours which entails the number of ours dedicated by the maintenance team to implementing maintenance tasks. Maintenance in web applications has two main components: content maintenance and technical maintenance.Content maintenance requires performing tasks of a non-technical nature like updating product inventory and information, adding news or articles, managing blog posts, updating contact or brand and visual identity information and moderating user comments or messages. Content maintenance team staffing and therefore content maintenance effort is determined by the web application's size [1].According to [1] and as presented in Table 1, content maintenance cost is determined by the number of hours required to keep the web application up to date in terms of content. The larger the application the more hours need to be invested in keeping it up to date in terms of content. The more content changes in an application the more technical maintenance tasks are generated. Technical maintenance tasks are generated because content managers spot bugs or request new functionality to help keep the content up to date. Therefore, content update frequency is a factor that impacts maintenance effort and cost.Technical maintenance requires adding new functionality, performing regular back-ups, monitoring website outages, checking IP reputation, checking DNS configuration, test website speed, check for broken links, perform software updates if third party software was used when developing the web application, monitor traffic statistics and review search engine indexing.According to [1] and as presented in Table 2, technical team stuffing is determined by the level of complexity built into the web application. Thus, a Basic web application which is characterized by static content build mainly using front-end technologies like HTML, CSS and JavaScript will require no more than 1 person to perform maintenance tasks. A Dynamic web application which stores its content in a database and has functionality which is powered by PHP, Java, ASP.NET, Python, Ruby or any other high-level programming language will require 1 to 2 persons to perform maintenance tasks. …

The geometrical dimensions of a counterflow natural draft wet-cooling tower are optimized to obtain the minimum combined operational and capital cost compounded over the economic life of the cooling tower. Wet-cooling tower performance evaluation software is employed in conjunction with an optimization algorithm to obtain the optimum cooling tower geometry. It is shown that the inlet height of the cooling tower, which primarily determines the operational cost, is the most critical parameter influencing the total operational and capital cost of a cooling tower. The effects of the diameter of the cooling tower and the volume of the concrete have a less pronounced effect on the total operational and capital cost of a cooling tower. The results of the mathematical optimization algorithm are verified by a manual investigation of the solution domain.

A tri-layer stochastic framework to manage electricity market within a smart community in the presence of energy storage systems

Biogas fueled combined cooling, desalinated water and power generation systems

Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm