Numerical model-based analysis of energy-efficient reverse osmosis (EERO) process: Performance simulation and optimization

The article presents a method for optimizing driving strategies aimed at minimizing energy consumption while driving. The method was developed for the needs of an electric powered racing vehicle built for the purposes of the Shell Eco-marathon (SEM), the most famous and largest race of energy efficient vehicles. Model-based optimization was used to determine the driving strategy. The numerical model was elaborated in Simulink environment, which includes both the electric vehicle model and the environment, i.e., the race track as well as the vehicle environment and the atmospheric conditions. The vehicle model itself includes vehicle dynamic model, numerical model describing issues concerning resistance of rolling tire, resistance of the propulsion system, aerodynamic phenomena, model of the electric motor, and control system. For the purpose of identifying design and functional features of individual subassemblies and components, numerical and stand tests were carried out. The model itself was tested on the research tracks to tune the model and determine the calculation parameters. The evolutionary algorithms, which are available in the MATLAB Global Optimization Toolbox, were used for optimization. In the race conditions, the model was verified during SEM races in Rotterdam where the race vehicle scored the result consistent with the results of simulation calculations. In the following years, the experience gathered by the team gave us the vice Championship in the SEM 2016 in London.

Download Full-text

Energy-efficient data centres : model-based analysis of power-performance trade-offs

10.3990/1.9789036546881 ◽

2018 ◽

Author(s):

Björn Frits Postema

Keyword(s):

Energy Efficient ◽

Power Performance ◽

Model Based ◽

Trade Offs ◽

Efficient Data ◽

Data Centres ◽

Model Based Analysis

Download Full-text

Model-Based Analysis of Feedback Control Strategies in Aerobic Biotrickling Filters for Biogas Desulfurization

Processes ◽

10.3390/pr9020208 ◽

2021 ◽

Vol 9 (2) ◽

pp. 208

Author(s):

Luis Rafael López ◽

Mabel Mora ◽

Caroline Van der Heyden ◽

Juan Antonio Baeza ◽

Eveline Volcke ◽

...

Keyword(s):

Closed Loop ◽

Control Strategies ◽

Biotrickling Filter ◽

Process Performance ◽

Control Loops ◽

Biotrickling Filters ◽

Model Based ◽

Outlet Concentration ◽

The Impact ◽

Model Based Analysis

Biotrickling filters are one of the most widely used biological technologies to perform biogas desulfurization. Their industrial application has been hampered due to the difficulty to achieve a robust and reliable operation of this bioreactor. Specifically, biotrickling filters process performance is affected mostly by fluctuations in the hydrogen sulfide (H2S) loading rate due to changes in the gas inlet concentration or in the volumetric gas flowrate. The process can be controlled by means of the regulation of the air flowrate (AFR) to control the oxygen (O2) gas outlet concentration ([O2]out) and the trickling liquid velocity (TLV) to control the H2S gas outlet concentration ([H2S]out). In this work, efforts were placed towards the understanding and development of control strategies in biological H2S removal in a biotrickling filter under aerobic conditions. Classical proportional and proportional-integral feedback controllers were applied in a model of an aerobic biotrickling filter for biogas desulfurization. Two different control loops were studied: (i) AFR Closed-Loop based on AFR regulation to control the [O2]out, and (ii) TLV Closed-Loop based on TLV regulation to control the [H2S]out. AFR regulation span was limited to values so that corresponds to biogas dilution factors that would give a biogas mixture with a minimum methane content in air, far from those values required to obtain an explosive mixture. A minimum TLV of 5.9 m h−1 was applied to provide the nutrients and moisture to the packed bed and a maximum TLV of 28.3 m h−1 was set to prevent biotrickling filter (BTF) flooding. Control loops were evaluated with a stepwise increase from 2000 ppmv until 6000 ppmv and with changes in the biogas flowrate using stepwise increments from 61.5 L h−1 (EBRT = 118 s) to 184.5 L h−1 (EBRT = 48.4 s). Controller parameters were determined based on time-integral criteria and simple criteria such as stability and oscillatory controller response. Before implementing the control strategies, two different mass transfer correlations were evaluated to study the effect of the manipulable variables. Open-loop behavior was also studied to determine the impact of control strategies on process performance variables such as removal efficiency, sulfate and sulfur selectivity, and oxygen consumption. AFR regulation efficiently controlled [O2]out; however, the impact on process performance parameters was not as great as when TLV was regulated to control [H2S]out. This model-based analysis provided valuable information about the controllability limits of each strategy and the impact that each strategy can have on the process performance.

Download Full-text