A System Dynamics Approach for Hospital Waste Management in a City in a Developing Country: The Case of Nablus, Palestine

Hospitals and health centers provide a variety of healthcare services and normally generate hazardous waste as well as general waste. General waste has a similar nature to that of municipal solid waste and therefore could be disposed of in municipal landfills. However, hazardous waste poses risks to public health, unless it is properly managed. The hospital waste management system encompasses many factors, i.e., number of beds, number of employees, level of service, population, birth rate, fertility rate, and not in my back yard (NIMBY) syndrome. Therefore, this management system requires a comprehensive analysis to determine the role of each factor and its influence on the whole system. In this research, a hospital waste management simulation model is presented based on the system dynamics technique to determine the interaction among these factors in the system using a software package, ithink. This model is used to estimate waste segregation as this is important in the hospital waste management system to minimize risk to public health. Real data has been obtained from a case study of the city of Nablus, Palestine to validate the model. The model exhibits wastes generated from three types of hospitals (private, charitable, and government) by considering the number of both inpatients and outpatients depending on the population of the city under study. The model also offers the facility to compare the total waste generated among these different types of hospitals and anticipate and predict the future generated waste both infectious and non-infectious and the treatment cost incurred

An Intelligent Fuzzy Logic Controller for Maximum Power Capture of Point Absorbers

This article presents an intelligent fuzzy logic controller (FLC) for controlling single-body heaving wave energy converter (WEC) or what is widely known as “Point Absorber”. The controller aims at maximizing the energy captured from the sea waves. The power take-off (PTO) limitations are addressed implicitly in the fuzzy inference system (FIS) framework. In order to enhance the WEC power capturing bandwidth and make it less susceptible to wave environment irregularities and the system parametric uncertainties, the controller is built to have a self-configurable capability. This also eliminates the need to repeatedly run in-situ tuning procedure of the fuzzy controller or switch between several controllers based on the operating conditions. The fuzzy membership functions (MFs) are optimally tuned using particle swarm optimization (PSO) algorithm. To alleviate the computational burden associated with performing on-line optimization, the fuzzy controller is tuned at a rate significantly lower than the system sampling time. The suggested PSO-FLC has shown promising results compared with the fixed structure fuzzy logic controller (FS-FLC) and other passive control strategies. Several computer simulations were carried out to evaluate the controller effectiveness by applying different sea-states and analyzing the resultant WEC dynamics

Robust and low computational cost controller for improving captured power in heaving wave energy converters

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