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The purpose of this paper is twofold: to analyze the computational complexity of the cogeneration design problem; to present an expert system to solve the proposed problem, comparing such an approach with the traditional searching methods available.

The complexity of the cogeneration problem is analyzed through the transformation of the well-known knapsack problem. Both problems are formulated as decision problems and it is proven that the cogeneration problem is np-complete. Thus, several searching approaches, such as population heuristics and dynamic programming, could be used to solve the problem. Alternatively, a knowledge-based approach is proposed by presenting an expert system and its knowledge representation scheme.

The expert system is executed considering two case-studies. First, a cogeneration plant should meet power, steam, chilled water and hot water demands. The expert system presented two different solutions based on high complexity thermodynamic cycles. In the second case-study the plant should meet just power and steam demands. The system presents three different solutions, and one of them was never considered before by our consultant expert.

The expert system approach is not a “blind” method, i.e. it generates solutions based on actual engineering knowledge instead of the searching strategies from traditional methods. It means that the system is able to explain its choices, making available the design rationale for each solution. This is the main advantage of the expert system approach over the traditional search methods. On the other hand, the expert system quite likely does not provide an actual optimal solution. All it can provide is one or more acceptable solutions.

This paper describes a method developed to schedule the preventive maintenance tasks in separate and linked cogeneration plants while satisfying the maintenance and production constraints.

The proposed methodology is based on a mixed integer programming model which finds the maximum number of available power and desalting units in separate and linked cogeneration plants. To verify that the model can be implemented for a real system, a case study of scheduling the preventive maintenance tasks of a cogeneration plant in Kuwait is illustrated.

An efficient solution can be achieved for scheduling the preventive maintenance tasks and production in cogeneration plants.

The paper offers a practical model that can be used to schedule preventive maintenance for expensive equipment in cogeneration plans.

The model presented is an effective decision tool that optimises the solution of the maintenance scheduling problem for cogeneration plants under maintenance and production constraints.

This study is aimed to develop a novel intuitionistic fuzzy P-graph with Gaussian membership function to help decision-makers deal with complex process network systems.

Two fuzzy P-graph case studies of the cogeneration system were selected, and relevant data were collected, including the structure and flow sequence of the system, and the rate of material and product transitions between the operating units. Gaussian function membership was set according to the restriction of fuzzy upper and lower bounds. Then the α-cut was used to obtain different upper and lower bound restrictions of each membership degree. After finding the optimal and suboptimal solutions for different membership degrees, the results of non-membership and hesitation were calculated.

The proposed method will help the decision maker consider the risk and provide more feasible solutions to choose the optimal and suboptimal solutions based on their own or through experience. The proposed model in this study has more flexibility in operation and decision making.

This study is the first to propose a novel intuitive fuzzy P-graph and demonstrates the effectiveness and flexibility of the method by two case studies of the cogeneration system. However, the addition of hesitation can increase the error tolerance of the system. Even for the solutions with a high degree of membership, optimal and suboptimal solutions still exist for the decision maker to select. Since decision makers expect the higher achievement of the target requirements; thus, it is important to have more feasible solutions with a high degree of membership.

The proposed solar thermal cooling cogeneration cycle is well suited for industrial as well as domestic needs and it eliminates need of electricity for refrigeration system. The purpose of this paper is to integrate power and cooling to minimize the energy usage.

The proposed plant has double turbine with superheater and reheater to extract more energy, operating on single generator. The saturated refrigerant from the exit of the generator is used to run the primary turbine and the exit mass of refrigerant is split into 50:50 cooling to power ratio.

It produces additional power of 24 kW at absorber concentration of 0.42 and turbine inlet concentration of 0.95, with separator temperature of 145°C and atmosphere temperature of 30°C.

The proposed cooling cogeneration cycle is possible to run on all the refrigerant working fluid mixture and it overcomes the problem of Goswami cycle which is not possible to run in hot climatic countries.

The cycle can operate individually as refrigeration cycle, power cycle and both and it will run all climatic conditions.

Agile manufacturers depend on low cost, abundant electricity to remain competitive in the global marketplace. Self‐generation of electricity or cogeneration of electricity and thermal energy at the manufacturer’s location can provide both economical and uninterrupted service. Generation methods are standby, peak‐shaving, baseload, commercial, and mobile generation. Each of these represents opportunities in agile manufacturing. The number of countries that have deregulated, market‐driven electrical utilities are growing and will include most of Europe and the USA by 2005. The demands of agile manufacturing are to produce high‐quality, market‐sensitive products at the lowest possible cost in an environment that has constant dynamic changes. Cogeneration will provide both flexible and cost‐efficient electricity as part of an overall energy strategy. Further, it will provide an agile energy resource that will complement the pursuit of competitive advantage in the global market for customized goods and services.

This paper aims to quantify the potentials for the development of combined heat and power (CHP) in Europe.

To this end, it uses the TIMES-EU energy-economic model and assesses the impact of key policy options and targets in the area of CO₂ emissions reduction, renewable energies and energy efficiency improvements. The results are also compared with the cogeneration potentials as reported by the Member States in their national reports.

The paper shows that CHP output could be more than doubled and that important CHP penetration potential exists in expanding the European district heating systems. This result is even more pronounced with the far-reaching CO₂ emissions reduction necessary in order to meet a long-term 2 degree target. Nevertheless, the paper also shows that strong CO₂ emission reductions in the energy sector might limit the CHP potential due to increased competition for biomass with the transport sector.

Given the proven socio-economic benefits of using CHP, the paper identifies the areas for future research in order to better exploit the potential of this technology such as the combination of CHP and district cooling or country- and industry-specific options to generate process heat.

To analyze the structural changes in electrical energy production sector caused by rapidly growing economy and Lithuania's international environmental commitments to the EU and international organizations.

The case study estimates the environmental‐financial aspects of cogeneration system to be implemented at the boiler house. After quantitative estimation of the electric energy demands the financial comparison of long and mid‐term environmental measures is presented.

The study responds to European strategy towards doubling the amount of cogenerated electrical power and provides financial‐environmental estimates of cogeneration installations.

Installation and maintenance of co‐generation system requires high investments, however, in a long‐term perspective it would result in a substantial environmental and financial effect.

The proposed scheme could be adjusted to the local conditions of the individual country as one of energy production options to bring about a sustainable energy future.

The analyzed quantitative assessment of national cogenerated energy potential serves as a tool for implementation of sustainable energy production in practice.

The purpose of this paper is to assess the carbon dioxide emissions associated with electric, HVAC, and hot water use from a US university.

First, the total on‐campus electrical, natural gas and oil consumption for an entire year was assessed. For each category of energy use, the carbon associated with consumption of a single unit was calculated. Using this, the total carbon dioxide emissions for the entire university were estimated.

It was found that the university's activities resulted in approximately 4 tons of carbon dioxide emissions per student per year. In total, the university emitted nearly 38,000 tons of carbon dioxide during the 2007 fiscal year. In addition, it was found that emissions from on‐campus steam production, which account for roughly 57 per cent of total CO₂ emissions, would be improved with the addition of two proposed cogeneration facilities.

The originality and value of this paper is attributed to: the recent international concern over CO₂ emissions and their global warming impact; the increasing adoption of the American College & University Presidents' Climate Commitment which in part calls for an inventory of campus emissions; and the underdeveloped research area relating to total university campus carbon footprint estimation.

The purpose of this paper is to describe a method that has been set up to schedule preventive maintenance (PM) tasks for power and water plants with all constraints such as production and maintenance.

The proposed methodology relies on the zero-one integer programming model that finds the maximum number of power and water units available in separate generating units. To verify this, the model was implemented and tested as a case study in Kuwait for the Cogeneration Station.

An effective solution can be achieved for scheduling the PM tasks and production at the power and water cogeneration plant.

The proposed model offers a practical method to schedule PM of power and water units, which are expensive equipment.

This proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for power and water units in a cogeneration plant, effectively and complies with all constraints.

The combined heat and power dispatch (CHPD) aims to optimize the outputs of online units in a power plant consisting thermal generators, co-generators and heat-only units. Identifying the operating point of a co-generator within its feasible operating region (FOR) is difficult. This paper aims to solve the CHPD problem in static and dynamic environments.

The CHPD plant operation is formulated as an optimization problem under static and dynamic load conditions with the objectives of minimizations of cost and emissions subject to various system and operational constraints. A novel bio-inspired search technique, grey wolf optimization (GWO) algorithm is used as an optimization tool.

The GWO-based algorithm has been developed to determine the preeminent power and heat dispatch of operating units within the FOR region. The proposed methodology provides fuel cost savings and lesser pollutant emissions than those in earlier reports. Particularly, the GWO always keeps the co-generator’s operating point within the FOR, whereas most of the existing methods fail.

The GWO is applied for the first time to solve the CHPD problems. New dispatch schedules are reported for 7-unit system with the objectives of total fuel cost and emission minimizations, 24-unit system for economic operation and 11-unit system in dynamic environment. The simulation experiments reveal that GWO converges quickly, consistent and the statistical performance clears its applicability to CHPD problems.