• Energy Research

SummaryStudies investigated the patient‐care (in‐hospital) and outside‐the‐hospital energy consumptions for delivering the hemodialysis (HD) service. A life cycle inventory methodology was used for this patient‐based analysis for two hospitals located in Wichita, Kansas. It was found that, for both hospitals, the actual HD machines consumed approximately 3.5 kilowatt‐hours (kWh) of electrical energy per HD, only 8% to 16% of the total energy used for delivering the HD service (in hospital). This increases to 9.6 to 28.9 kWh of hospital billable energy for the whole system of HD machine, auxiliaries, and dialysis water treatment. Converting these hospital direct electrical energy values to natural resource energy (nre) then adding the cradle‐to‐gate natural resource energy for the manufacturing and supply chain of all the HD consumables, the total is 78 to 149 kWh nre/HD. The nre measures all the direct fuel burned to generate energy and is thus directly related to emissions to the air, water, and land and is a direct secondary impact on public health from HD. The ratio of outside‐the‐hospital energy to direct hospital HD electrical energy consumption is 4:1 to 7:1, so a broader base exists for improvement than just the hospital.

In this study, a parallel replacement problem with retrofitting (PRP-R) model is proposed to determine the trade-off between retrofitting and replacing an asset. The primary objective is to identify the replacement, maintenance and retrofitting schedule that optimise purchasing new assets, operation and maintenance (O&M) cost, and retrofitting cost under budget and production constraints resulting in a mixed-integer linear programming formulation. This model is applied to a case study of energy industry involving wind turbines (WTs). Results show that due to a lower O&M cost, retrofitting is less costly than keeping the WTs. In addition, the effects of key parameters such as O&M cost, retrofitting cost, budget allocated for retrofitting and governmental subsidy on the optimal replacement policy on total cost are studied. This research contributes a model that can be used to determine if WT retrofitting is economically justified and provides a rigorous analytical framework for optimising the decision-making process over the wind farm life cycle.

Appropriate maintenance can prolong the life of an asset and prevent costly breakdowns that may result in lost production, failed shipping schedules and a decline in customer satisfaction. The goal of this study was to present a comprehensive framework that utilises sustainability metrics based on social, environmental and economic criteria to select an appropriate maintenance strategy among a variety of maintenance strategy alternatives, such as preventive, failure-based, reliability-centred, condition-based and total productive maintenance strategies, for a manufacturing company. The sustainability-based approach may significantly influence the personnel, energy, material and the overall costs in a company. In the first step of this paper, a sustainability-based decision-making structure is proposed for the maintenance strategy selection problem applying the concept of factor analysis to determine the leading factors in each of the sustainability pillars. In the next step, a fuzzy VIKOR framework is use...

Click on the DOI link to access the article (may not be free). ; In this study, a mixed-integer linear programming (MILP) modelling approach is proposed to identify the optimum maintenance or retrofitting schedule under budget and energy production constraint(s) by improving failure rate of assets. The proposed reliability/availability asset management with retrofitting (RAAMWR) model seeks to maximise the total net profit subject to achieving a target reliability/availability value and minimise the total improvement cost subject to a budgetary constraint. We apply our model to a case study involving wind turbines (WTs). The results of this study show that to reach the target reliability value with improved failure rate data, model selects retrofitting due to lower loss time and high energy production rate of retrofitting options. This optimal retrofitting choice is not only due to low loss time, but also improving the existing failure rate of an asset to reach the target reliability. In addition, the effects of key parameters on total cost, such as operation and maintenance (O&M) cost, retrofitting cost, budget allocated for retrofitting, and different target reliability values on the optimal improvement policy were considered.

Energy-aware scheduling in a manufacturing environment with real-time energy pricing is a challenging problem. The purpose of this paper is to study a nonpreemptive scheduling problem on a single-machine to minimize the total tardiness and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To achieve these objectives, we develop several new holistic genetic algorithms. The proposed model is solved via several methods including weighted sum method and multiobjective genetic algorithms based on dominance rank (GA-1), weighted sum aggregation (GA-2), dominance ranking procedure and crowding distance comparison (GA-3), and heuristic approach (GA-H). This paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges. Thus, the findings provide information on when to start each job. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we illustrate how the results of the multiobjective model could be utilized in decision making using the technique for order preference by similarity to ideal solution method.

Energy is an expensive resource that is becoming more scarce with increasing population and demand. In this paper, a mathematical model to minimize energy consumption and reduce total completion time of a single machine is proposed, and a multiobjective genetic algorithm is utilized to obtain an approximate set of nondominated alternatives. Furthermore, dominance rules and a heuristic are proposed to increase the speed of the proposed genetic algorithm. Finally, the analytical hierarchical process is utilized to select a solution with some additional criteria.

A long-term multiobjective model for the power generation expansion planning of electric systems is described and evaluated in this paper. The model optimizes simultaneously multiple objectives (i.e., minimizes costs, environmental impact, imported fuel and fuel price risks) and decides the location of the planned generation units in a multiperiod planning horizon. Among the attributes considered in the model are the investment and operation cost of the units, the environmental impact, the amount of imported fuel, and the portfolio investment risk. The approach to solve this problem is based on multiobjective linear programming and the analytical hierarchy process. A case study from the Mexican Electric Power System is used to illustrate the proposed framework

Click on the DOI to access this article (may not be free). ; Advancements in digital processes and additive manufacturing technology enable the production produce complex-shaped metallic components by depositing material, layer upon layer, in precise geometric shapes. However, the additively manufactured components may still need some additional machining to meet specific requirements. With this aim, the present work deals with the machining of additively manufactured SS 316L under four different cooling environments, namely dry, flood, minimum quantity lubrication (MQL), and cryogenic carbon dioxide (CO$_2$). The tool wear, surface roughness, cutting temperature, and hardness were evaluated using various cooling methods. Additionally, residual stress was explored with the help of XRD data. The results of the experiments show that cryogenic CO$_2$ performs well in terms of cooling and reduces friction between the tool and the work material, thereby reducing tool wear. Under cryogenic cooling, the surface finish was improved by 43–46%, 33–36%, and 15–17% over dry, flood, and MQL conditions, respectively.

In this study, we analyze an integrated production-preventive maintenance planning problem where product processing times are affected by machine degradation. Preventive maintenance and repair can return the degraded machines/equipment to normal conditions, which can improve the job processing times and the amount of energy consumed. We propose a mathematical model for an integrated production and preventive maintenance planning problem in a multi-product, multi-period, single-machine manufacturing environment with minimal repair and energy consumption considerations in order to minimize the overall cost of production, inventory, energy, maintenance, and repair. The model investigates the impact of imperfect and “as-good-as-new” (AGAN) maintenance strategies on production plan and total cost. Experimental analyses provide insights about the proposed model: when the sensitivity of processing times to machine health status increases, the required number of maintenance actions increases. In addition, enforcing more number of maintenance actions into a production plan decreases energy cost of the system.

In this paper, we present a fuzzy multi-objective model for power generation expansion planning problem with uncertainty in objective functions and demand balance constraints. The purpose of the fuzzy generation expansion model is to determine the optimal generation amount in existing and new generation units and the optimal transmission network, given that decision maker may tolerate some violation in constraints and objective functions. A fuzzy linear programming approach is utilised to transform the multiobjective fuzzy generation expansion model into a linear program. To illustrate the proposed model, a Mexican electric power system case is studied. A comprehensive sensitivity analysis on the impact of different objective functions, environmental impact of different fuel types and changes in demand is done to illustrate the capability of the proposed model.