1 - Adsorption desalination—Principles, process design, and its hybrids for future sustainable desalination

Recent developments in thermally-driven seawater desalination: Energy efficiency improvement by hybridization of the MED and AD cycles

Hybrid systems in seawater desalination-practical design aspects, status and development perspectives

Batch reverse osmosis (BRO)-adsorption desalination (AD) hybrid system for multipurpose desalination and minimal liquid discharge

Life-cycle cost analysis of adsorption cycles for desalination

Hollow fiber microporous membranes have been used widely in membrane distillation (MD) for seawater desalination applications due to their high surface area. The configurations of feed inlets and outlets significantly impact the performance of hollow fiber direct contact membrane distillation (HF-DCMD). An increase in feed inlets and outlets can also profoundly affect the system’s performance by reducing the concentration polarization at the membrane surface and increasing the mass transfer rate. These aspects are critical when designing and optimizing HF-DCMD systems for various applications such as water desalination and wastewater treatment. Computational fluid dynamics (CFD) simulations were implemented to investigate the performance of HF-DCMD for a desalination process. The effect of inlet and outlet configuration in a tubular shell filled with hollow fiber membranes is examined in modules with a 31% packing ratio. The feed solution with a concentration of 35 g/L NaCl flows in the shell side while the permeated water flows inside the hollow fibers. The Navier-Stokes, mass and energy transport equations for laminar flows are solved with a coupled boundary condition to model the desalination process in the module. Various feed inlet and outlet configurations are considered, including one with an inlet and outlet normal to the shell’s axis (at the shell’s end surfaces) and others with inlets and outlets parallel to the axis. The module with an inlet and outlet at each shell end parallel performs better than other configurations. The permeate flux is enhanced by approximately 153% when four feed inlets are placed at the side wall of the shell. On the other hand, the study observed a slight increase in water production when multiple feed outlets are used.

Optimization of sustainable seawater desalination: Modeling renewable energy integration and energy storage concepts

Using the sun to co-generate electricity and freshwater

Both brackish water desalination and seawater desalination processes are well established and in common use around the globe to create new water supply sources. The farther the location of the source water from the ocean or seashore, the lower the salinity (TDS) of the water and the lower the osmotic pressure that needs to be overcome when desalinated water is produced. This is one of the major reasons that brackish desalination is often considered less costly than seawater desalination. A number of project considerations, however, indicate that seawater desalination can be beneficial and more cost-effective than brackish water desalination. To make a fair comparison, we need to properly compare all major aspects of both types of projects to define the best and most appropriate desalination technology. While brackish water has less feed water TDS, it is more challenging to dispose of the produced concentrate. Also, although brackish water desalination needs less energy to overcome osmotic pressure, it usually requires more energy to draw the water from the well than it takes to pump seawater from the open ocean intake. Another factor is that the temperature of the brackish well water may be lower than the temperature of ocean water, giving seawater desalination an advantage in energy demand. In comparing brackish to seawater desalination, these major aspects should be evaluated: (1) Locations of seawater and brackish water plants, relative to the major consumers of the desalinated water, (2) Transportation (pumping and disposal) costs of the feed water and produced water, (3) Potential colocation of a seawater plant with a large industrial user (e.g., power plant) of the seawater for cooling or other purposes, (4) Produced quality of brackish water and seawater desalination in terms of major minerals and emerging contaminants, (5) Sustainability of the water source: capacity and depth of the brackish water wells, as well as the type of soil. (6) Technical and economic aspects of produced concentrate disposal, (7) Permitting process costs for brackish and seawater desalination, and (8) The economics of both brackish and seawater desalination treatment processes: capital costs, operational and maintenance (O&M) costs, lifetime water cost, and total water cost (TWC). This paper discusses the major evaluation criteria and considerations involved in properly comparing the economic and technical aspects of brackish and seawater desalination to determine the more favorable desalination technology for a given desalination project.

Performance investigation of an advanced multi-effect adsorption desalination (MEAD) cycle

An offshore gas-producing well recently encountered an increase in water production. The well featured multiple completion zones, creating ambiguity and uncertainty in the water source and production zone. Due to the significant deviation of the well, coiled tubing (CT) provided the means of conveyance for an advanced array of well diagnostic tools selected to evaluate the multiple downhole scenarios that may lead to the increase in water production, including completion failures, saturation depletion, and flow from unexpected sources. This paper discusses the integrated analysis and methodology behind the successful determination of the water source in an offshore gas-producing well. The deployed tools include a multi-detector pulsed-neutron tool (MDPNT), an advanced acoustic array leak detection tool (ALDT), and a suite of production logging tools. The logging suite was tailored to address the wealth of possible scenarios for the increase in water production. Logging was performed in shut-in and flowing environments to understand the behavior of the well in dynamic conditions. Following the acquisition, the data sets were integrated and analyzed, leading to the successful determination of the problem zone. The well featured two perforated reservoirs, each separated by packers and sliding sleeves. The deeper zone was determined to be water-saturated and should have been isolated by a closed sliding sleeve beneath a packer. MDPNT oxygen activation eliminated the possibility of channeling between the water-producing zone and the gas-producing zone. The packers in the well proved to be intact based on the interpretation of the ALDT acoustic noise data and the count rate reading of the MDPNT. The investigation then focused on the completion string jewelry and involved computing a two-dimensional (2D) radial flow map from beamforming of the ALDT-recorded acoustic noise activity around the wellbore, which revealed a closed sliding sleeve within the water-producing zone was likely leaking and, therefore, ineffective. The saturation results using the MDPNT in sigma mode indicated that the perforated reservoirs contained a low remaining gas saturation. Following the diagnosis, the operator ensured the sliding sleeve was closed reducing water production by more than 50%, resulting in an improved well performance. Compatibility with CT enabled the advanced suite of pulsed neutron, acoustic noise array, and PL wireline sensors to be deployed in a well with significant deviation. As the water production source was unknown, many possible scenarios had to be tested. Careful job planning and interpretation of the acquired log data eliminated scenarios and led to the conclusion that the completion integrity was compromised at a sliding sleeve, therefore enabling flow from a water-saturated zone.

Hybrid systems in seawater desalination—practical design aspects, present status and development perspectives

Recycling brine water of reverse osmosis desalination employing adsorption desalination: A theoretical simulation

Microbubble aeration enhances performance of vacuum membrane distillation desalination by alleviating membrane scaling

Large-scale deep neural networks (DNNs) are both computing and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, model size is an essential factor affecting performance, scalability, and energy efficiency. Non-structured Weight pruning achieves good compression ratios but suffers from three drawbacks: 1) the irregular network structure after pruning, which affects performance and throughput; 2) increased training complexity; and 3) lack of rigorous guarantee of compression ratio and inference accuracy. To overcome these limitations, this work presents CIRCNN, a principled approach to represent weights and process neural networks using block circulant matrices, a structured representation. CIRCNN utilizes Fast Fourier Transform (FFT)- based fast multiplication, simultaneously reducing the computational complexity (both in inference and training) from O(n2) to O(n log n) and storage complexity from O(n2) to O(n), with negligible accuracy loss. Compared to other approaches, CIRCNN is distinct due to its mathematical rigor: the DNNs based on CIRCNN can converge to the same "effectiveness" as DNNs without compression. We present the CIRCNN architecture, a universal DNN inference engine that can be implemented in various hardware/software platforms with configurable network architecture (layer type, size, scales, etc.). In CIRCNN architecture, due to recursive property, FFT can be used as the vital computing kernel, which ensures universal and small-footprint implementations. The compressed but regular network structure avoids the pitfalls of network pruning and facilitates high performance and throughput with a highly pipelined and parallel design. To demonstrate performance and energy efficiency, we test CIRCNN in field-programmable gate arrays (FPGAs), application-specific integrated circuit (ASIC), and embedded processors. Our results show that CIRCNN architecture achieves very high energy efficiency and performance with a small hardware footprint. Based on FPGA implementation and ASIC synthesis results, CIRCNN achieve a 6 - 102× improvement in energy efficiency compared with state-of-the-art results. To further accelerate computation and facilitate hardware implementation, we present CIRCNN-DR, a block circulant matrix-based DNN architecture with decoupled and reconfigurable computing modules. CIRCNN-DR advances the original CIRCNN architecture with three improvements. First, due to linear property, FFT and inverse FFT (IFFT) are decoupled: IFFTs are performed after accumulating element-wise multiplications results (i.e., FFT (wij ) ◦ FFT (xj )). FFT-IFFT decoupling reduces the amount of computation, and, more importantly, allows the same unified computing modules to be reconfigured to perform either FFTs/IFFTs or multiplications and additions (MACs) in three consecutive phases (i.e., FFT-MAC-IFFT), - the key for implementation with limited hardware resources. Second, CIRCNN-DR leverages batch processing to reduce pipeline bubbles and timing overhead in the deep pipeline. Third, we present an algorithm and hardware co-optimization framework. CIRCNN-DR is realized with end-to-end FPGA implementations and ASIC synthesis. Under the same accuracy level, our FPGA implementation achieves 31× and 70× energy efficiency improvement compared to state-of-the-art FPGA implementations and IBM TrueNorth processor, respectively. Compared with CIRCNN, the CIRCNN-DR framework achieves at least a 50% improvement in performance and energy efficiency. For ASIC implementations, the presented CIRCNN-DR exhibits impressive advantages in terms of power, throughput, and energy efficiency. Experimental results indicate that this method is greatly suitable for applying DNNs onto both FPGAs and mobile/IoT devices. Next, we present design automation on top of block circulant matrix-based weight repre- sentation. we present a design automation framework and implementation optimization for real-time implementation of sound recognition. We adopt the block circulant matrix method from prior work. We overcome limitations by developing an HLS automated framework to translate high-level language such as C/C++ to the hardware description language Verilog, to overcome complicated data dependency and skewed computations, and to accelerate the development cycle. To achieve real-time, highly-efficiency implementation of both applications on a single FPGA, we present hardware implementation optimization techniques based on the PE level. Overall, compared with the state-of-the-art LSTM implementation, the presented C-LSTM designs generated by our framework achieve up to an 18.8× and 33.5× gains in performance and energy efficiency with small accuracy degradation, respectively. Experimental results show that our presented design automation and optimization techniques can significantly compress LSTM models while introducing negligible accuracy degradation. To further bridge the gap between algorithm and hardware platforms, we present REQ, a resource-aware, efficient weight quantization framework for DNNs by maximally exploiting both software and hardware-level optimization opportunities on FPGAs. We present a heterogeneous weight quantization method, including both equal-distance and mixed powers-of-two methods considering hardware resource. Compared to Titan X-YOLO, our GPU implementation achieves up to 2.9× enhancement in energy efficiency. Compared to the GPU-based YOLO implementation with the best energy efficiency (our TX2-YOLO), our two FPGA YOLO implementations achieve a 3.5× and 5.8× improvement in energy efficiency.

Hybrid membrane system for desalination and wastewater treatment : Integrating forward osmosis and low pressure reverse osmosis