Wastewater treatment plant (WWTP) utilization of combined heat and power (CHP) systems allows for the efficient use of on-site biogas production, as well as increased annual savings in utility costs. In this paper, a review of biogas energy recovery options, CHP prime mover technologies, and the costs associated with biogas cleaning give a broad summary of the current state of CHP technology in WWTPs. Even though there are six different prime mover technologies, the main ones currently being implemented in WWTPs are micro turbines, fuel cells and reciprocating engines. Different prime movers offer varying efficiencies, installation costs, and biogas impurity (H₂S, siloxanes, HCl) tolerances. To evaluate the long-term savings capabilities, a techno-economic assessment of a CHP installation at a case study WWTP shows the payback, annual savings, and initial costs associated with the installation of a CHP system. In this case, a study a payback of 5.7 years and a net present value of USD 709,000 can be achieved when the WWTP generates over 2,000,000 m³ of biogas per year and utilizes over 36,000 GJ of natural gas per year.

The traditional balanced assembly line designs can perform quite inefficiently under the presence of high labor turnover, low operator learning rates, and stochastic processing times. In these situations assembly line designs based on dynamic work allocation and work sharing principles have been shown to render a higher and less variable throughput. However, for situations where low labor turnover conditions and high operator learning rates exist, the traditional balanced lines still tend to be most productive. This paper has two objectives: to introduce a method based on work sharing principles, which we denominate Modified Work Sharing (MWS), and to develop some simple analytical tools that will allow us to compare the performance of this method with the traditional and other dynamic work allocation line designs. These results suggest that the traditional method is the most affected by the introduction of new operators in the production lines and thus the most affected by variability in general. On the other hand, dynamic work allocation methods appear to better absorb the variability introduced by new operators by sharing the workload of new operators with the more experienced members of the line.

The cold chain—the system of refrigerated storage and transport that provides fresh produce or other essentials to be maintained at desired temperatures and environmental conditions—is responsible for substantial energy consumption and greenhouse gas (GHG) emissions, and failures in the cold chain lead to food and energy waste. Here, we introduce the mini container concept as an alternative to conventional reefers, particularly for small growers. Mini containers are relatively small, insulated boxes, with environmental conditions controlled by an electric-powered central driving unit, which can be aggregated as needed and transported by non-refrigerated trucks and trailers. We analyze the energy consumption and GHG emissions for the transport of tomatoes in two cities representing contrasting climates, Phoenix, Arizona, and Chicago, Illinois, for conventional reefers and the proposed mini containers. These two cities provide the opportunity to compare the energy consumption and GHG emissions for the proposed mini containers versus conventional refrigerated transport under extremely different climate conditions. The results show that, as expected in both cases, as the ambient air temperature increases, the energy consumption and GHG emissions also increase. For partial reefer loads less than 72% and 85% for Phoenix and Chicago, respectively, the use of the mini containers reduces energy consumption and GHG emissions because of the reduced volume requiring refrigeration. In general, since the mini containers are fully electrified, their corresponding GHG emissions can be dramatically reduced, and since the fresh produce can be pre-cooled with renewable energy, GHG emissions can even be eliminated.

This article presents a methodology to measure relative industrial energy efficiency across plants within a manufacturing sector through the use of energy-production signatures (EPSs). Linear programming in combination with regression methods, benchmarking and simulation models are used to study the behavior of a representative manufacturing plant. The methodology is validated using data available from the Department of Energy database. The proposed EPSs can be used for proactive benchmarking and diagnostic purposes leading to improvements in energy consumption for individual companies. The results show that the proposed methodology successfully identifies energy and production inefficiencies within a manufacturing segment. Copyright © 2010 John Wiley & Sons, Ltd.