An Approach to Optimize Multi-family Residence HVAC Systems Using Digitalization

As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Geographic,2017).Major urban centers are committed to reducing greenhouse gases by 80%by 2050(IEA,2021).However,achieving such goals in rental properties is not easy.Landlords are hesitant to use high-efficiency technologies because,typically,tenants pay the utilities bill.However,that situation is rapidly changing.For example,New York City like other US cities,is considering a carbon cap on all large buildings(Local Law 97,2019).That means landlords will pay a carbon penalty if the building’s carbon footprint exceeds certain threshold no matter who uses that carbon.The Pacific Northwest National Laboratory(PNNL)has received funds from DOE(US Department of Energy)with the collaboration of a commercial partner to address emerging energy efficiency market opportunity in multi-family or rental housing as discussed above.It has partnered with a large national real estate owner in order to test a novel energy optimization method at a rental property in Tempe,Arizona.By using a seamless-integrated method of acquiring building’s operating data,the optimization approach essentially resets setpoints of different energy consuming equipment such as chillers,boilers,pumps,and fans.Data-driven optimization approach is pragmatic and easily transferrable to other buildings.The authors shall share the problem background,technical approach,and preliminary results.

Effects of water-based binders on electrochemical performance of manganese dioxide cathode in mild aqueous zinc batteries

In the majority of rechargeable batteries including lithium-ion batteries,polyvinylidene fluoride(PVdF)binders are the most commonly used binder for both anode and cathode.However,using PVdF binder requires the organic solvent of N-methyl-2-pyrrolidone which is expensive,volatile,combustible,toxic,and has poor recyclability.Therefore,switching to aqueous electrode processing routes with non-toxic binders would provide a great leap forward towards the realization of ideally fully sustainable and environmentally friendly electrochemical energy storage devices.Various water-soluble binders(aqueous binders)were characterized and compared to the performance of conventional PVdF.Our study demonstrates that the electrochemical performance of Zn/MnO_(2) aqueous batteries is significantly improved by using sodium carboxymethyl cellulose(CMC)binder.In addition,CMC binders offer desirable adhesion,good wettability,homogeneous material distribution,and strong chemical stability at certain pH levels(3.5-5)without any decomposition for long-cycle life.

Fabrication,characterization and optimization of high conductivity and high quality nanocrystalline molybdenum thin films

The present study investigated the influence of substrate temperature(Ts)and working pressure(P(Ar))on tailoring the properties of nanocrystalline(nc)molybdenum(Mo)films fabricated by radio-frequency magnetron sputtering.The structural,morphological,electrical and optical properties of nc-Mo films were evaluated in detail.The Mo films exhibited(110)orientation with average crystallite size varying from 9 to 22(±1)nm on increasing Ts.Corroborating with structural data,the electrical resistivity decreased from 55μΩcm to 10μΩcm,which is the lowest among all the Mo films.For Mo films deposited under variable P(Ar).the(110)peak intensity decrement coupled with peak broadening on increasing P(Ar).Lower deposition pressure yielded densely packed thin films with superior structural properties along with low resistivity of 15μΩcm.Optimum conditions to produce high quality Mo films with excellent structural,morphological,electrical and optical characteristics for utilization in solar cells as back contact layers were identified.

Bridging cognitive gaps between user and model in interactive dimension reduction

Interactive machine learning(ML)systems are difficult to design because of the‘‘Two Black Boxes’’problem that exists at the interface between human and machine.Many algorithms that are used in interactive ML systems are black boxes that are presented to users,while the human cognition represents a second black box that can be difficult for the algorithm to interpret.These black boxes create cognitive gaps between the user and the interactive ML model.In this paper,we identify several cognitive gaps that exist in a previously-developed interactive visual analytics(VA)system,Andromeda,but are also representative of common problems in other VA systems.Our goal with this work is to open both black boxes and bridge these cognitive gaps by making usability improvements to the original Andromeda system.These include designing new visual features to help people better understand how Andromeda processes and interacts with data,as well as improving the underlying algorithm so that the system can better implement the intent of the user during the data exploration process.We evaluate our designs through both qualitative and quantitative analysis,and the results confirm that the improved Andromeda system outperforms the original version in a series of high-dimensional data analysis tasks.