Towards Optimal Energy-Water Supply System Operation for Agricultural and Metropolitan Ecosystems

The energy-water demands of metropolitan regions and agricultural ecosystems are ever-increasing. To tackle this challenge efficiently and sustainably, the interdependence of these interconnected resources has to be considered. In this work, we present a holistic decision-making framework which takes into account simultaneously a water and energy supply system with the capability of satisfying metropolitan and agricultural resource demands. The framework features: (i) a generic large-scale planning and scheduling optimization model to minimize the annualized cost of the design and operation of the energy-water supply system, (ii) a mixed-integer linear optimization formulation, which relies on the development of surrogate models based on feedforward artificial neural networks and first-order Taylor expansions, and (iii) constraints for land and water utilization enabling multi-objective optimization. The framework provides the operational profiles of all energy-water system elements over a given time horizon, which uncover potential synergies between the essential food, energy, and water resource supply systems.Comment: Part of the Foundations of Computer-Aided Process Operations and Chemical Process Control (FOCAPO/CPC) 2023 Proceeding

Fuel consumption and CO2 emissions of passenger cars over the New Worldwide Harmonized Test Protocol

AbstractIn 2014 the United Nations Economic Commission for Europe (UNECE) adopted the global technical regulation No. 15 concerning the Worldwide harmonized Light duty Test Procedure (WLTP). Having significantly contributed to its development, the European Commission is now aiming at introducing the new test procedure in the European type-approval legislation for light duty vehicles in order to replace the New European Driving Cycle (NEDC) as the certification test.The current paper aims to assess the effect of WLTP introduction on the reported CO2 emissions from passenger cars presently measured under the New European Driving Cycle and the corresponding test protocol. The most important differences between the two testing procedures, apart from the kinematic characteristics of the respective driving cycles, is the determination of the vehicle inertia and driving resistance, the gear shifting sequence, the soak and test temperature and the post-test charge balance correction applied to WLTP. In order to quantify and analyze the effect of these differences in the end value of CO2 emissions, WLTP and NEDC CO2 emission measurements were performed on 20 vehicles, covering almost the whole European market. WLTP CO2 values range from 125.5 to 217.9g/km, NEDC values range from 105.4 to 213.2g/km and the ΔCO2 between WLTP and NEDC ranges from 4.7 to 29.2g/km for the given vehicle sample. The average cold start effect over WLTP was found 6.1g/km, while for NEDC it was found 12.3g/km. For a small gasoline and a medium sized diesel passenger car, the different inertia mass and driving resistance is responsible 63% and 81% of the observed ΔCO2 between these two driving cycles respectively, whereas the other parameters (driving profile, gear shifting, test temperature) account for the remaining 37% and 19%

Inclusion of information costs in process design optimization under uncertainty

Recent developments in process design have focused on establishing optimization-based approaches to support decision-making under uncertainty, but few efforts have been made to study and consider how information regarding this uncertainty affects optimal decision. In this paper we develop an optimal design framework that, besides integrating process profitability, robustness and quality issues, allows one to decide how much it is worth to spend in research and experimentation for selectively reducing parameter uncertainties and guiding R&D activities. The design problem is thus formulated as a stochastic optimization problem, whose objective function includes an information cost term, leading to the identification of optimal parameter uncertainty levels one should end up with, as well as the corresponding amounts to be spent in R&D. A case study comprising a reactor and heart exchanger system is introduced and provides an illustrative application for the suggested methodology.http://www.sciencedirect.com/science/article/B6TFT-448HNR0-80/1/6d7a6dc558dcacffcefc4d8b3d65005

Quality costs and robustness criteria in chemical process design optimization

The identification and incorporation of quality costs and robustness criteria is becoming a critical issue while addressing chemical process design problems under uncertainty. This article presents a systematic design framework that includes Taguchi loss functions and other robustness criteria within a single-level stochastic optimization formulation, with expected values in the presence of uncertainty being estimated by an efficient cubature technique. The solution obtained defines an optimal design, together with a robust operating policy that maximizes average process performance. Two process engineering examples (synthesis and design of a separation system and design of a reactor and heat exchanger plant) illustrate the potential of the proposed design framework. Different quality cost models and robustness criteria are considered, and their influence in the nature and location of best designs systematically studied. This analysis reinforces the need for carefully considering/addressing process quality and robustness related criteria while performing chemical process plant design.http://www.sciencedirect.com/science/article/B6TFT-4292GMD-4/1/407d80598ce29b51434c5bbbe89e234