Alkali activated fuel ash and slag mixes:optimization study from paste to concrete building blocks

Alkali activated binders, based on ash and slag, also known as geopolymers, can play a key role in reducing the carbon footprint of the construction sector by replacing ordinary Portland cement in some concretes. Since 1970s, research effort has been ongoing in many research institutions. In this study, pulverized fuel ash (pfa) from a UK power plant, ground granulated blast furnace slag (ggbs) and combinations of the two have been investigated as geopolymer binders for concrete applications. Activators used were sodium hydroxide and sodium silicate solutions. Mortars with sand/binder ratio of 2.75 with several pfa and ggbs combinations have been mixed and tested. The optimization of alkali dosage (defined as the Na2O/binder mass ratio) and modulus (defined as the Na2O/SiO2 mass ratio) resulted in strengths in excess of 70 MPa for tested mortars. Setting time and workability have been considered for the identification of the best combination of pfa/ggbs and alkali activator dosage for different precast concrete products. Geopolymer concrete building blocks have been replicated in laboratory and a real scale factory trial has been successfully carried out. Ongoing microstructural characterization is aiming to identify reaction products arising from pfa/ggbs combinations.

Optimization of Controller Parameters for Energy Saving

Among various technologies to tackle the twin challenges of sustainable energy supply and climate change, energy saving through advanced control plays a crucial role in decarbonizing the whole energy system. Modern control technologies, such as optimal control and model predictive control do provide a framework to simultaneously regulate the system performance and limit control energy. However, few have been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper investigates the correlations between control energy consumption and system performance using two popular control approaches widely used in the industry, namely the PI control and subspace model predictive control. Our investigation shows that the controller design is a delicate synthesis procedure in achieving better trade-o between system performance and energy saving, and proper choice of values for the control parameters may potentially save a significant amount of energy

Optimization of singlet oxygen production from photosensitizer-incorporated, medically relevant hydrogels

Photodynamic therapy and photodynamic antimicrobial chemotherapy are widely used, but despite this, the relationships between fluence, wavelength of irradiation and singlet oxygen ((1) O2 ) production are poorly understood. To establish the relationships between these factors in medically relevant materials, the effect of fluence on (1) O2 production from a tetrakis(4-N-methylpyridyl)porphyrin (TMPyP)-incorporated 2-hydroxyethyl methacrylate: methyl methacrylate: methacrylic acid (HEMA: MMA:MAA) copolymer, a total energy of 50.48 J/cm(2) , was applied at varying illumination power, and times. (1) O2 production was characterized using anthracene-9,10-dipropionic acid, disodium salt (ADPA) using a recently described method. Using two light sources, a white LED array and a white halogen source, the LED array was found to produce less (1) O2 than the halogen source when the same power (over 500 - 600 nm) and time conditions were applied. Importantly, it showed that the longest wavelength Q band (590 nm) is primarily responsible for (1) O2 generation, and that a linear relationship exists between increasing power and time and the production of singlet oxygen. 

Building information modeling based building design optimization for sustainability

Environmental problems, especially climate change, have become a serious global issue waiting for people to solve. In the construction industry, the concept of sustainable building is developing to reduce greenhouse gas emissions. In this study, a building information modeling (BIM) based building design optimization method is proposed to facilitate designers to optimize their designs and improve buildings’ sustainability. A revised particle swarm optimization (PSO) algorithm is applied to search for the trade-off between life cycle costs (LCC) and life cycle carbon emissions (LCCE) of building designs. In order tovalidate the effectiveness and efficiency of this method, a case study of an office building is conducted in Hong Kong. The result of the case study shows that this method can enlarge the searching space for optimal design solutions and shorten the processing time for optimal design results, which is really helpful for designers to deliver an economic and environmental friendly design scheme.

Joint Optimization of Area Spectral Efficiency and Delay over PPP Interfered Ad-Hoc Networks

To evaluate the performance of the co-channel transmission based communication, we propose a new metric for area spectral efficiency (ASE) of interference limited ad-hoc network by assuming that the nodes are randomly distributed according to a Poisson point processes (PPP). We introduce a utility function, U = ASE/delay and derive the optimal ALOHA transmission probability p and the SIR threshold τ that jointly maximize the ASE and minimize the local delay. Finally, numerical results have been conducted to confirm that the joint optimization based on the U metric achieves a significant performance gain compared to conventional systems.

Cell Coverage Optimization for the Multicell Massive MIMO Uplink

We investigate the cell coverage optimization problem for the massive multiple-input multiple-output (MIMO) uplink. By deploying tilt-adjustable antenna arrays at the base stations, cell coverage optimization can become a promising technique which is able to strike a compromise between covering cell-edge users and pilot contamination suppression. We formulate a detailed description of this optimization problem by maximizing the cell throughput, which is shown to be mainly determined by the user distribution within several key geometrical regions. Then, the formulated problem is applied to different example scenarios: for a network with hexagonal shaped cells and uniformly distributed users, we derive an analytical lower bound of the ergodic throughput in the objective cell, based on which, it is shown that the optimal choice for the cell coverage should ensure that the coverage of different cells does not overlap; for a more generic network with sectoral shaped cells and non-uniformly distributed users, we propose an analytical approximation of the ergodic throughput. After that, a practical coverage optimization algorithm is proposed, where the optimal solution can be easily obtained through a simple one-dimensional line searching within a confined searching region. Our numerical results show that the proposed coverage optimization method is able to greatly increase the system throughput in macrocells for the massive MIMO uplink transmission, compared with the traditional schemes where the cell coverage is fixed.

On the statistical memory architecture exploration and optimization

The worsening of process variations and the consequent increased spreads in circuit performance and consumed power hinder the satisfaction of the targeted budgets and lead to yield loss. Corner based design and adoption of design guardbands might limit the yield loss. However, in many cases such methods may not be able to capture the real effects which might be way better than the predicted ones leading to increasingly pessimistic designs. The situation is even more severe in memories which consist of substantially different individual building blocks, further complicating the accurate analysis of the impact of variations at the architecture level leaving many potential issues uncovered and opportunities unexploited. In this paper, we develop a framework for capturing non-trivial statistical interactions among all the components of a memory/cache. The developed tool is able to find the optimum memory/cache configuration under various constraints allowing the designers to make the right choices early in the design cycle and consequently improve performance, energy, and especially yield. Our, results indicate that the consideration of the architectural interactions between the memory components allow to relax the pessimistic access times that are predicted by existing techniques.

Good Laboratory Practice for optimization research

Good Laboratory Practice has been a part of non-clinical research for over 40 years. Optimization Research, despite having many papers discussing standards being published over the same period of time, has yet to embrace standards that underpin its research. In this paper we argue the need to adopt standards in optimization research. Building on previous papers, many of which have suggested that the optimization research community should adopt certain standards, we suggest a concrete set of recommendations that the community should adopt. We also discuss how the proposals in this paper could be progressed.

Towards rational catalyst design: a general optimization framework

Rational catalyst design is one of the most fundamental goals in heterogeneous catalysis. Herein, we briefly review our previous design work, and then introduce a general optimization framework, which converts catalyst design into an optimization problem. Furthermore, an example is given using the gradient ascent method to show how this framework can be used for rational catalyst design. This framework may be applied to other design schemes.