Different attempts have been made to facilitate successful operation of Wastewater Pumping (WWP) system. The WWP units which are already existed in different parts of the world have been studied to identify its success, failure and different parameters associated with its suboptimal performance. The performance of WWP depends on three parameters namely pump, hydraulics, control system and pump station. These parameters are interdependent and must be carefully matched to achieve efficient WWP system. Nowadays the scenario has changed where organizations has started looking increasingly at the total cost of ownership, another way of saying Life Cycle Cost Analysis (LCCA) and recognizing the need to get most out of their equipment purchase. The master thesis includes theory part which describes the different parameters associated with WWP unit especially focusing on Xylems WWP system. This thesis is an attempt to help companies to know how LCCA could be productive management tool in order to minimize maintenance cost and maximize energy efficiency The study reported in this thesis work has been conducted to shed light over the use of Life Cycle Cost Analysis in WWP system. The current study tries to suggest and assess an adopted approach to ensure successful and efficient operation of WWP system with lowering energy demand and decrease in maintenance cost. Initial cost, Maintenance cost and Energy costs are important issues in the operation of WWP system since they are responsible for total cost over time. Therefore, description of each cost, formulas necessary for LCC calculations, data and survey structure, material and energy flow has been described. This work also aims to provide an extensive literature review, different survey and data collection techniques, analysis of collected data, statistical modelling, customer interaction by questionnaires and an interview with experts were used. LCC calculations were used to support the design and selection of most cost-efficient WWP system. Therefore, the given thesis work is an attempt to achieve better functional performance, improve existing design principles associated with WWP System, contribution to asses economic viability, support decision making to enhance operational quality to achieve efficient and successful WWP system.

We consider a class of periodic preventive maintenance (PM) optimization problems, for a single piece of equipment that deteriorates with time or use, and can be repaired upon failure, through corrective maintenance (CM). We develop analytical and simulation-based optimization models that seek an optimal periodic PM policy, which minimizes the sum of the expected total cost of PMs and the risk-averse cost of CMs, over a finite planning horizon. In the simulation-based models, we assume that both types of maintenance actions are imperfect, whereas our analytical models consider imperfect PMs with minimal CMs. The effectiveness of maintenance actions is modeled using age reduction factors. For a repairable unit of equipment, its virtual age, and not its calendar age, determines the associated failure rate. Therefore, two sets of parameters, one describing the effectiveness of maintenance actions, and the other that defines the underlying failure rate of a piece of equipment, are critical to our models. Under a given maintenance policy, the two sets of parameters and a virtual-age-based age-reduction model, completely define the failure process of a piece of equipment. In practice, the true failure rate, and exact quality of the maintenance actions, cannot be determined, and are often estimated from the equipment failure history. We use a Bayesian approach to parameter estimation, under which a random-walk-based Gibbs sampler provides posterior estimates for the parameters of interest. Our posterior estimates for a few datasets from the literature, are consistent with published results. Furthermore, our computational results successfully demonstrate that our Gibbs sampler is arguably the obvious choice over a general rejection sampling-based parameter estimation method, for this class of problems. We present a general simulation-based periodic PM optimization model, which uses the posterior estimates to simulate the number of operational equipment failures, under a given periodic PM policy. Optimal periodic PM policies, under the classical maximum likelihood (ML) and Bayesian estimates are obtained for a few datasets. Limitations of the ML approach are revealed for a dataset from the literature, in which the use of ML estimates of the parameters, in the maintenance optimization model, fails to capture a trivial optimal PM policy. Finally, we introduce a single-stage and a two-stage formulation of the risk-averse periodic PM optimization model, with imperfect PMs and minimal CMs. Such models apply to a class of complex equipment with many parts, operational failures of which are addressed by replacing or repairing a few parts, thereby not affecting the failure rate of the equipment under consideration. For general values of PM age reduction factors, we provide sufficient conditions to establish the convexity of the first and second moments of the number of failures, and the risk-averse expected total maintenance cost, over a finite planning horizon. For increasing Weibull rates and a general class of increasing and convex failure rates, we show that these convexity results are independent of the PM age reduction factors. In general, the optimal periodic PM policy under the single-stage model is no better than the optimal two-stage policy. But if PMs are assumed perfect, then we establish that the single-stage and the two-stage optimization models are equivalent. / text