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1. Variable-Capacity Operations with Modular Transits for Shared-Use Corridors

   https://doi.org/10.1177/0361198120928077  

   Shi, Xiaowei; Chen, Zhiwei; Pei, Mingyang; Li, Xiaopeng (September 2020, Transportation Research Record: Journal of the Transportation Research Board)

   null (Ed.)

   Since passenger demand in urban transit systems is asymmetrically distributed across different periods in a day and different geographic locations across the cities, the tradeoff between vehicle operating costs and service quality has been a persistent problem in transit operational design. The emerging modular vehicle technology offers us a new perspective to solve this problem. Based on this concept, we propose a variable-capacity operation approach with modular transits for shared-use corridors, in which both dispatch headway and vehicle capacity are decision variables. This problem is rigorously formulated as a mixed integer linear programming model that aims to minimize the overall system cost, including passenger waiting time costs and vehicle operating costs. Because the proposed model is linear, the state-of-the-art commercial solvers (e.g., Gurobi) can be used to obtain the optimal solution of the investigated problem. With numerical experiments, we demonstrate the feasibility of the mathematical model, verify the effectiveness of the proposed model in reducing overall system costs in transit systems, as well as the robustness of the proposed model with different parameter settings. 

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2. Optimization of a Do-It-Yourself Air Cleaner Design to Reduce Residential Air Pollution Exposure for a Community Experiencing Environmental Injustices

   https://doi.org/10.3390/atmos14121734  

   Sankhyan, Sumit; Clements, Nicholas; Heckman, Allison; Hollo, Aniya K.; Gonzalez-Beltran, Dulce; Aumann, Jonathan; Morency, Cora; Leiden, Luke; Miller, Shelly L. (December 2023, Atmosphere)

   The large-scale deployment of Do-it-yourself (DIY) air cleaners, especially in communities that historically bear the brunt of air pollution exposure-related injustices, provides communities a cost-effective option to reduce personal indoor exposure to particulate matter. In this study, we developed nine air cleaner prototypes, altering filter depth and the number and type of filters, and compared their PM2.5 removal effectiveness and maintenance-related parameters prior to deployment in North Denver, Colorado homes. Prototypes containing multiple high efficiency particulate air filters with a minimum reporting value of 13 (MERV13) had higher clean air delivery rates (CADR, >300 m3 h−1) compared to prototypes using a single filter (100–200 m3 h−1), but single-filter designs had comparable values of CADR normalized by initial and annual operating costs. Based on performance, cost, build time, and feedback from the community regarding concerns related to volatile organic compound exposure, the selected prototype (P9) used a combination of an activated carbon filter and single MERV13 filter with a 10.16 cm (4-inch) depth. Following this assessment, 120 of the selected air cleaner prototypes were built and deployed in homes around the communities in North Denver for two separate cohorts; feedback regarding their usage over the course of the deployment showed that in addition to the increased noise levels perceived by the participants, factors such as cold air flow from the air cleaner impacting the thermal comfort and aesthetics of the design reduced their usage time in homes. Future designs of DIY air cleaners could incorporate this feedback to help design improved features such as quieter air cleaners and real-time pollutant monitoring feedback to prompt users to keep them operational at all times of the day. 

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3. Supply Air Temperature Prediction in an Air-Handling Unit Using Artificial Neural Network

   https://doi.org/10.1115/IMECE2018-88507  

   Sukthankar, Nikita; Walekar, Abhishek; Agonafer, Dereje (November 2018, ASME 2018 International Mechanical Engineering Congress and Exposition)

   Continuous provision of quality supply air to data center’s IT pod room is a key parameter in ensuring effective data center operation without any down time. Due to number of possible operating conditions and non-linear relations between operating parameters make the working mechanism of data center difficult to optimize energy use. At present industries are using computational fluid dynamics (CFD) to simulate thermal behaviour for all types of operating conditions. The focus of this study is to predict Supply Air Temperature using Artificial Neural Network (ANN) which can overcome limitations of CFD such as high cost, need of an expertise and large computation time. For developing ANN, input parameters, number of neurons and hidden layers, activation function and the period of training data set were studied. A commercial CFD software package 6sigma room is used to develop a modular data center consisting of an IT pod room and an air-handling unit. CFD analysis is carried out for different outside air conditions. Historical weather data of 1 year was considered as an input for CFD analysis. The ANN model is “trained” using data generated from these CFD results. The predictions of ANN model and the results of CFD analysis for a set of example scenarios were compared to measure the agreement between the two. The results show that the prediction of ANN model is much faster than full computational fluid dynamics simulations with good prediction accuracy. This demonstrates that ANN is an effective way for predicting the performance of an air handling unit. 

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4. Nonlinear predictive regulation of an integrated green hydrogen and ammonia production system under time-varying renewable energy supply

   https://doi.org/10.1016/j.compchemeng.2025.109376  

   Cabral, Thiago Oliveira; Pourkargar, Davood B (January 2026, Computers & Chemical Engineering)

   This work presents a comprehensive model for a modular system that integrates green hydrogen and ammonia production with renewable energy generation. The chemical module comprises a high-temperature water electrolyzer for hydrogen production and an ammonia synthesis reactor. When solving the models over time, the system exhibits complex yet predictable dynamics, with the chemical module having a much faster response than other components. Under typical weather conditions, the renewable energy module generates over 50 kW for most of the day, partially meeting the chemical module’s energy demands. Nonlinear model predictive control (NMPC) is employed to manage the operation of the chemical module in response to variable renewable energy availability. The proposed NMPC framework determines the optimal supplemental energy required from the conventional energy grid to sustain the process. When renewable energy availability is high, the controller minimizes grid energy usage, maintaining the chemical module near its desired operating conditions with minimal reliance on external sources. Conversely, during low renewable energy availability periods, the controller increases grid energy acquisition to ensure stable system operation, demonstrating a greater dependence on external energy supplies. 

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5. Dynamic risk-based process design and operational optimization via multi-parametric programming

   Ali, M; Cai, X; Khan, F I; Pistikopoulos, E N; Tian, Y (April 2023, Digital chemical engineering)

   We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, as an explicit function of safety–critical process variables deviation from nominal operating conditions. Process design-aware risk-based multi-parametric model predictive control strategies are then derived which offer the advantages to: (i) integrate safety–critical variable bounds as path constraints, (ii) control risk based on multivariate process dynamics under disturbances, and (iii) provide model-based risk propagation trend forecast. A dynamic optimization problem is then formulated, the solution of which can yield optimal risk control actions, process design values, and/or real-time operating set points. The potential and effectiveness of the proposed approach to systematically account for interactions and trade-offs of multiple decision layers toward improving process safety and efficiency are showcased in a real-world example, the safety–critical control of a continuous stirred tank reactor at T2 Laboratories. 

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