Key Operation Research Articles

Protein Shift – How Membrane Technology Can Contribute

AbstractProteins are not only important for our daily diet but also one of the most important food ingredients worldwide. Since the 1970ies membrane processes have established themselves as a key unit operation in protein processing, e.g., in the dairy industry. The recent trend of the protein shift moving consumption of animal‐based to plant‐based proteins provides new applications for membrane processes. Thus, in this work, the opportunities to transfer the membrane processing experiences from animal‐based proteins to plant‐based protein products will be the focus.

Experimental investigation of gaseous solvent huff-n-puff in the Middle Bakken Formation

The efficacy of gaseous solvents in enhancing oil recovery (EOR) in unconventional reservoirs and the influence of operational and design parameters are still debated among the oil recovery research community. This work investigated the recovery-enhancing capabilities of two potent gaseous solvents, CO2 and ethane, in tight core samples. Laboratory huff-n-puff (H-n-P) experiments were conducted under three miscibility conditions to investigate the influence of the key operating parameters and the dependency of their impact on the miscibility conditions and gas composition. The results show that oil recovery increased with increasing pressure from below (BM) to above (AM) miscibility pressure in a non-linear trend, irrespective of the gas composition. Furthermore, the influence of the soak period was noticeably dependent on the miscibility condition, specifically more remarkable under AM conditions and less apparent under BM conditions. Likewise, the effect of the production period was more pronounced at AM conditions for both gases. Finally, the impact of rock surface area-to-volume (SA/V) was only observed at BM, where both gases recovered more oil in the core samples with high SA/V. In general, ethane showed a higher efficacy for oil recovery than CO2; CO2 recovered 21%–70% of oil in small core samples, while ethane could recover 32%–88%. The highest recovery was achieved with ethane injected under AM conditions, with a prolonged soak time, a short production period and into a core sample with a high SA/V. We believe the findings from this work will help better understand and design H-n-P EOR projects.

Improving the control of pharmaceutical compounds in activated sludge wastewater treatment plants: Key operating conditions and monitoring parameters

The control of pharmaceutical compounds (PhCs) in urban wastewater treatment plants (WWTPs) is an issue of health and environmental relevance, expected to become mandatory in the European Union and beyond, but studies at full scale are still scarce. A long-term (2.5-year) full-scale study addressing practical strategies for controlling PhCs was conducted in two activated sludge WWTPs with nitrification (BEI with A2O process; FNW with oxidation ditch). The results showed similar removal patterns in both plants – some of the most abundant influent PhCs were highly removed, others presented intermediate and variable removals, and some were recalcitrant. These behaviours validated a 4-class (A-D) PhC framework (based on biodegradation (kbio) and solid–water distribution (Kd)) for interpreting and predicting PhC removal, particularly sensitive to kbio, with a turning point at 1 L/(gSS.d). A statistical analysis conducted to unveil the key operation variables showed T254 is a good indicator of PhC removal and confirmed that favouring conditions for nitrifiers to grow is an operational strategy to improve PhC control. In BEI, F/M (food to microorganisms ratio) showed the most consistent role (over the sludge retention time), values below 0.08 d−1 being associated with effluent PhC concentrations 26–45 % lower. In FNW, the nitrification role was statistically expressed by the alkalinity reduction, values above 40 % (i.e., Nt-removals >80 %) being associated with effluent PhC concentrations 23–61 % lower.

Dynamic characteristics of a mechanically coupled organic Rankine-vapor compression system for heat-driven cooling

There are large quantities of compression heat wasted and cooling capacity required in cryogenic air separation units (ASUs). Mechanically coupled organic Rankine-vapor compression (ORVC) system is an attractive technology to realize heat-driven refrigeration for compression heat recovery and compression power saving in cryogenic ASUs. As the states of the compression heat inevitably change with the ambient environment, off-design operation of the ORVC system is worth investigating. A mechanically coupled ORVC test bench is designed and built in this paper. Off-design experiments are carried out to figure out the dynamic response characteristics and system performance variations. Results show that, the ORC working fluid flowrate and the opening of the throttle valve are the key operation parameters affecting the system dynamic response. The experimental system responds quickly with delay time less than 10 s and response time less than 1 min. The opening of the throttle valve has the greatest impact on the cooling capacity and system COP which reaches up to 0.61. This research may provide reference for control scheme design and actual operation for the compression heat recovery system in cryogenic ASUs.

Measurement-device-independent continuous variable quantum key distribution protocol operation in optical transport networks

Measurement-device-independent continuous variable quantum key distribution protocol operation in optical transport networks

Efficient purification of aqueous solutions contaminated with sulfadiazine by coupling electro-Fenton/ultrasound process: optimization, DFT calculation, and innovative study of human health risk assessment.

In the current work, the hybrid process potential of ultrasound (US) and electro-Fenton (EF), named sono-electro-Fenton (SEF), was fully investigated for sulfadiazine (SDZ) degradation. The decontamination in the integration approach was revealed to be greater than in individual procedures, i.e., EF process (roughly 66%) and US process (roughly 15%). The key operating process factors (i.e., applied voltage, H2O2 content, pH, initial concentration of SDZ, and reaction time) affecting SDZ removal were evaluated and optimized using Box-Behnken Design (BBD). In addition, an adaptive neuro-fuzzy inference system (ANFIS) as an efficient predictive model was applied to forecast the decontamination efficiency of SDZ through the SEF process based on the same findings produced from BBD. The results revealed that the predictability of SDZ elimination by the ANFIS and BBD approaches exhibited an excellent agreement (a greater R2 of 0.99%) among the both models. Density functional theory was also employed to forecast the plausible decomposition elucidation by the bond-breaking mechanism of organic substances. Plus, the main side products of SDZ degradation during the SEF process were tracked. Eventually, the non-carcinogenic risk assessment of different samples of natural water containing SDZ that was treated by adopting US, EF, and SEF processes was examined for the first time. The findings indicated that the non-carcinogenic risk (HQ) values of all the purified water sources were computed in the permissible range.

A 24.1 TOPS/W mixed-signal BNN processor in 28-nm CMOS

ABSTRACT A mixed-signal binarized neural network (BNN) processor for the MNIST image classification is demonstrated based on analogue circuit networks. BNN algorithm for training neural networks with binary weights and activations reduces power consumption and memory size. This algorithm is a design to perform core operations of multi-layer perceptron (MLP) to reduce complexity and power consumption using analogue circuits. The mixed-signal BNN processor employs a current mirror neuron to perform multiply-and-accumulate (MAC) operations and sign activation functions. The near-threshold current mirror neuron that computes the key operations of the BNN algorithm is used to achieve low power consumption. The design occupies 0.065 mm2 in 28-nm CMOS with 560B of on-chip SRAM. The 28-nm CMOS test-chip achieves energy efficiency of 24.1 TOPS/W and 94% accuracy on the MNIST image classification. This design with binary weights and activations exhibits only 5% degradation in accuracy compared with the models with floating-point precision.

Hydrogen production from biomass via moving bed sorption-enhanced reforming: Kinetic modeling and process simulation

This study explored a process intensification approach for hydrogen production through biomass gasification. Sorption-enhanced reforming (SER) in an autothermal countercurrent moving bed reformer is proposed as an alternative to multiple units traditionally required for syngas conditioning. This process utilizes the selective sorption and catalytic properties of calcium oxide to integrate multiple reforming reactions into a moving bed reactor. A multistage kinetic model was used to analyze and optimize the key operating parameters of the reformer. Results showed that the moving bed reformer completely converts all tars and increases the hydrogen concentration of syngas from 23 mol% to 80 mol% (dry basis). A comparison between equally sized models showed that the moving bed reformer outperforms a fluidized bed reformer under similar conditions. Simulation of the entire process showed that the hydrogen yield of the proposed SER process is 5% higher than the conventional process. In addition, the proposed process produces surplus electricity, while the conventional process has a 26% power deficit. The overall efficiency of the proposed process is 3.5% higher than the conventional process. The improved process efficiency, coupled with the potentially lower costs of a streamlined process, indicates that the proposed process is a promising route for green hydrogen production.

Pyrolysis and Extraction of Bark in a Biorefineries Context: A Critical Review

Bark-based biorefineries are estimated to become a trending topic in the coming years, particularly with their adsorbent applications and antioxidant production. While the potential benefits of bark-based biorefineries are well-recognized, it is not known how to develop a bark-based biorefinery considering different unit operations and the potential end products. The characterization and screening of different barks for chemical composition is the initial step in biorefinery development, along with the selection of the relevant conversion processes. This state-of-the-art review provides background knowledge on the pyrolysis and extraction studies of bark as the key conversion operations. The results of recent (2010–2022) bark pyrolysis and extraction studies were critically analyzed for process conditions, product properties, and product yields, as well as a discussion of energy-saving possibilities. A biorefinery scheme was proposed based on these data. The current knowledge gaps were identified and future directions were evaluated, which include the production of charcoals, platform chemicals, and extract profiling for specific applications. The results indicate that barks are particularly rich in hydrophilic extractives with potential antioxidant properties and pyrolysis operations, resulting in functional chars that may be used in value-added applications as adsorbent materials. A biorefinery scheme allows for the production of platform chemicals, antioxidant extracts and biochars from barks while contributing to the reduction of waste and environmental pollution.

FPGA Implementation of Compact Hardware Accelerators for Ring-Binary-LWE-based Post-quantum Cryptography

Post-quantum cryptography (PQC) has recently drawn substantial attention from various communities owing to the proven vulnerability of existing public-key cryptosystems against the attacks launched from well-established quantum computers. The Ring-Binary-Learning-with-Errors (RBLWE), a variant of Ring-LWE, has been proposed to build PQC for lightweight applications. As more Field-Programmable Gate Array (FPGA) devices are being deployed in lightweight applications like Internet-of-Things (IoT) devices, it would be interesting if the RBLWE-based PQC can be implemented on the FPGA with ultra-low complexity and flexible processing. However, thus far, limited information is available for such implementations. In this article, we propose novel RBLWE-based PQC accelerators on the FPGA with ultra-low implementation complexity and flexible timing. We first present the process of deriving the key operation of the RBLWE-based scheme into the proposed algorithmic operation. The corresponding hardware accelerator is then efficiently mapped from the proposed algorithm with the help of algorithm-to-architecture implementation techniques and extended to obtain higher-throughput designs. The final complexity analysis and implementation results (on a variety of FPGAs) show that the proposed accelerators have significantly smaller area-time complexities than the state-of-the-art designs. Overall, the proposed accelerators feature low implementation complexity and flexible processing, making them desirable for emerging FPGA-based lightweight applications.

A review of system topologies, key operation and control technologies for offshore wind power transmission based on HVDC

AbstractOffshore wind farm (OWF) is considered as a perfect zero‐carbon energy source for the future power system. However, the growing offshore distance and water depth of OWF make the OWF HVDC transmission technique a more promising solution than HVAC due to higher cost‐efficiency and reliability. In this paper, the current situation of OWF‐HVDC projects is introduced at first. Then, novel converter topologies with the higher power density and cost‐efficiency are presented, including the hybrid modular multilevel converter (MMC), alternative arm converter (AAC), and diode rectifier (DR). Next, several OWF HVDC transmission system topologies are introduced, including terminal‐hybrid, station‐hybrid and all‐DC delivered system. Furthermore, the key technologies for OWF HVDC operation and control are summarized, including grid‐forming control strategy for offshore wind turbines, stability analysis method, corresponding stability enhancement measures and frequency support control strategies. Additionally, the fault ride‐through and protection strategies for different fault locations have been presented. Finally, the main conclusions and prospects for OWF HVDC are summarized.

Numerical Study of Dry Reforming of Methane in Packed and Fluidized Beds: Effects of Key Operating Parameters

Replacing the conventionally used steam reforming of methane (SRM) with a process that has a smaller carbon footprint, such as dry reforming of methane (DRM), has been found to greatly improve the industry’s utilization of greenhouse gases (GHGs). In this study, we numerically modeled a DRM process in lab-scale packed and fluidized beds using the Eulerian–Lagrangian approach. The simulation results agree well with the available experimental data. Based on these validated models, we investigated the effects of temperature, inlet composition, and contact spatial time on DRM in packed beds. The impacts of the side effects on the DRM process were also examined, particularly the role the methane decomposition reaction plays in coke formation at high temperatures. It was found that the coking amount reached thermodynamic equilibrium after 900 K. Additionally, the conversion rate in the fluidized bed was found to be slightly greater than that in the packed bed under the initial fluidization regime, and less coking was observed in the fluidized bed. The simulation results show that the adopted CFD approach was reliable for modeling complex flow and reaction phenomena at different scales and regimes.

A dual perspective of organizational resilience (OR) and information technology systems resilience (ITSR): an analysis of interdependencies and tensions

Contemporary organizations face a rising incidence of disasters, extreme events and crises (Boin and Lodge 2021). In parallel, the socioeconomic landscape is increasingly complex which intensifies inter-organizational dependencies and the risk of cascading failures (Ansell et al. 2021). To survive and perhaps thrive, organizations must cultivate organizational resilience (OR). However, their capacity to do so is currently curtailed. Although it is widely recognized that the performance of organizational processes, functions and capabilities is closely aligned to the effectiveness of associated information technology systems (ITS) (Schultze and Orlikowski 2004); models of OR have yet to elucidate mechanisms by which ITS contribute to resilience capabilities (Annarelli and Nonino 2016). In this conceptual paper, which is a work in progress, we reflect upon the nature of interdependencies and tensions between ITS resilience (ITSR) and OR. We adopt a deductive, qualitative approach to systematically compare OR & ITSR. Our comparative analysis is informed by OR models described by Duchek (2020), Sheffi and Rice Jr (2005) and Weick and Sutcliffe (2011), while for ITSR, we employ the Reactive Manifesto as interpreted by Bonér et al. (2014) and Debski et al. (2017). Figs 1, 2 illustrate our interpretation of each resilience construct. As may be seen on Fig. 1 we interpret OR as a three-phase process including preemptive, proactive & recovery phases each possessing associated resilience capabilities. The process may occasion three operational outcomes (or levels of resilience maturity). 1st , 2nd and 3rd order resilience denote organizations that when a disruption occurs, merely maintain key operations, rapidly achieve a return to normal operations or that capitalize on a disruption to achieve an improved post-shock trajectory respectively. merely maintain key operations, rapidly achieve a return to normal operations or that capitalize on a disruption to achieve an improved post-shock trajectory respectively. ITSR is interpreted via the notion, reactive scalability which describes an organizational system that is both responsive and scalable (Debski et al. 2017) i.e., which is able to rapidly achieve (responsive) appropriately dimensioned (scalable) adaptations to intra and extra-organizational changes. Our analysis employs a multi-level approach. The preliminary results of the analysis are presented in Table 1. Upon completion of the analysis, we will elaborate theoretical propositions pertaining to the relationship between the OR and ITSR constructs to guide subsequent empirical research to bridge the theoretical divide between these in reality, indissociable resilience constructs. This comprises the main anticipated contribution.

Optical Logic Gates Based on Z-Shaped Silicon Waveguides at 1.55 μm.

In the last ten years, silicon photonics has made considerable strides in terms of device functionality, performance, and circuit integration for a variety of practical uses, including communication, sensing, and information processing. In this work, we theoretically demonstrate a complete family of all-optical logic gates (AOLGs), including XOR, AND, OR, NOT, NOR, NAND, and XNOR, through finite-difference-time-domain simulations using compact silicon-on-silica optical waveguides that operate at 1.55 μm. Three slots, grouped in the shape of the letter Z, make up the suggested waveguide. The function of the target logic gates is based on constructive and destructive interferences that result from the phase difference experienced by the launched input optical beams. These gates are evaluated against the contrast ratio (CR) by investigating the impact of key operating parameters on this metric. The obtained results indicate that the proposed waveguide can realize AOLGs at a higher speed of 120 Gb/s with better CRs compared to other reported designs. This suggests that AOLGs could be realized in an affordable manner and with improved outcomes to enable the satisfaction of the current and future requirements of lightwave circuits and systems that critically rely on AOLGs as core building elements.

A hyperchaotic image encryption algorithm based on LSTM neural network and lifting wavelet transform

In order to solve the problems of simple permutation-diffusion structure, low encryption efficiency and small chaos range of existing chaotic systems, this paper proposes a hyperchaotic image encryption algorithm based on LSTM neural network and lifting wavelet transform. By building upon the Lorenz chaotic system, we construct a new hyperchaotic system in this paper, which has more complex dynamic characteristics and higher Lyapunov exponent. Then, the image is encrypted by lifting wavelet transform and zigzag conversion algorithm. Finally, the trained LSTM neural network is used to process the Henon chaotic sequence to obtain the second key for encryption, which can effectively resist chosen-plaintext attack. The experimental findings indicate that the proposed encryption algorithm has good performance in key security, differential attack, statistical analysis and operation efficiency, which indicates that the algorithm has high security.

Human Factors in the Development of the Hydraulic System of the Powered Roof Support

The growing demand for mineral resources, including hard coal, requires the continuous improvement and development of technologies to improve safety, productivity and performance. The implementation of automation and digitization in mines has been ongoing for several years. Machinery and equipment development and improvement is also ongoing. The authors discuss the development and improvement of powered roof support, which is a critical part of the longwall system. The powered roof support is responsible for the safety of equipment and people working in the longwall. It supports the face and immediate roof and moves the longwall system forward as the shearer removes the coal and the roof collapsing (goafing) behind the shields. The aim of this study was to examine the human factors involved in the development and improvement of the hydraulic support system of powered roof support. The research was carried out among employees in hard coal mines using mainly surveys. The research allowed the authors to examine and analyze the information provided by the miners concerning powered roof support in the era of Industry 4.0 (the Fourth Industrial Revolution). According to the research findings, miners expect improvement in the reliability of the powered roof support, and they want the key operating parameters, especially the required load capacity, to be monitored in real-time so adjustments and improvements can be made. The results of the research point out the needs and expectations of miners. Their opinions should determine the direction and any research gaps into powered roof support.

Truss optimization with frequency constraints using the medalist learning algorithm

Though the truss optimization with frequency constraints is very complicated due to its high nonlinear property, the importance and necessity of truss optimization in the real engineering design render it attractive for many researchers. This paper proposed a new heuristic algorithm inspired by the observation of the learning behavior of individuals in a group for the truss optimization with frequency constraints. The new swarm intelligent heuristic algorithm called the medalist learning algorithm has a concise implementation procedure composed of two key operations: identifying the medalists and conducting the individual learning. The learning period is divided into predefined learning stages. In each learning stage, a learning efficiency is generated using a learning function originated from the natural learning function. The key operations mentioned above are repeated in each learning stage. For an excellent performer called medalist, to conduct individual learning is to carry out self-learning trials; but for a common learner, to conduct individual learning is to learn from his/her past experience and imitate the medalists. Six well-known truss optimization problems were employed to verify the efficiency of the new algorithm. The results obtained by the medalist learning algorithm were compared with those previously reported in literature. The new algorithm outperforms the others in terms of the best and average weights. The computational cost measured by the number of the function estimations of the structure for the new algorithm to obtain the best design usually is in the middle of the reported costs. In some cases, the new algorithm has the lowest worst weight and smallest number of function estimations.

Using Artificial Intelligence to Enhance Key Business Operations

Using Artificial Intelligence to Enhance Key Business Operations

Ascent Similarity Caching With Approximate Indexes

Similarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented reality applications. When these systems need to serve large objects with tight delay constraints, edge servers close to the end-user can operate as similarity caches to speed up the retrieval. In this paper we present, a new similarity caching policy which improves on the state of the art by using (i) an (approximate) index for the whole catalog to decide which objects to serve locally and which to retrieve from the remote server, and (ii) a mirror ascent algorithm to update the set of local objects with strong guarantees even when the request process does not exhibit any statistical regularity.

Progressive Feature Transmission for Split Classification at the Wireless Edge

We consider the scenario of inference at the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wire-less edge</i> , in which devices are connected to an edge server and ask the server to carry out remote classification, that is, classify data samples available at edge devices. This requires the edge devices to upload high-dimensional features of samples over resource-constrained wireless channels, which creates a communication bottleneck. The conventional feature pruning solution would require the device to have access to the inference model, which is not available in the current split inference scenario. To address this issue, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">progressive feature transmission</i> (ProgressFTX) protocol, which minimizes the overhead by progressively transmitting features until a target confidence level is reached. A control policy is proposed to accelerate inference, comprising two key operations: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">importance-aware feature selection</i> at the server and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transmission-termination control</i> . For the former, it is shown that selecting the most important features, characterized by the largest discriminant gains of the corresponding feature dimensions, achieves a sub-optimal performance. For the latter, the proposed policy is shown to exhibit a threshold structure. Specifically, the transmission is stopped when the incremental uncertainty reduction by further feature transmission is outweighed by its communication cost. The indices of the selected features and transmission decision are fed back to the device in each slot. The control policy is first derived for the tractable case of linear classification, and then extended to the more complex case of classification using a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">convolutional neural network</i> . Both Gaussian and fading channels are considered. Experimental results are obtained for both a statistical data model and a real dataset. It is shown that ProgressFTX can substantially reduce the communication latency compared to conventional feature pruning and random feature transmission strategies.