Multi-Objective Analysis of a Gas-to-Liquid (GTL) Process from Economic, Safety, and Environmental Perspectives

One of the most important challenges facing chemical engineers today is developing more efficient processes that reduce the discharge of greenhouse gasses (GHG) and the usage of material and energy resources. Furthermore, industrial manufacturers are making major efforts to incorporate inherently safer design concepts when developing or retrofitting processes. With the recent discoveries of shale gas, there is a growing interest in monetization pathways that convert gas to chemicals and fuels. The Fisher-Tropsch gas-to-Liquid (GTL) process is regarded as a promising alternative to producing liquid transportation fuels. A typical GTL plant requires substantial mass, energy, and financial resources. The syngas production section, in particular, accounts for approximately 50-75% of the total capital costs and about 60-70% of the total energy requirements. Also, the GTL plants have several trains for the syngas production section to accommodate large-scale capacities. Focus on this work is to investigate possible improvements to the GTL process in two areas: 1) tailgas recycling and 2) lower steam-to-carbon (S/C) ratio for autothermal reforming (ATR). The results from these cases are analyzed in terms of cost, inherent safety, and environmental sustainability. Ultimately, the aim of this research is to support the decision makers in understanding the multi-objective insights and in using these insights to make better decisions in design and operation. This study provides a comparative approach for four different operating cases from various perspectives: economics, inherent safety, and environmental sustainability. In the inherent safety analysis, a fire and explosion hazard analysis are used to choose the least hazardous material for a fuel. The release rate is estimated at the failure case in order to evaluate the degree of containment loss. For the environmental sustainability, the carbon efficiency of the overall process and CO2 emissions are evaluated. The operating conditions and results are validated against pilot test results from industry in order to verify the degree of carbon deposition during operation. The results are used to establish tradeoffs among the various objectives

Web Service Based Universal Management of Workflow Resources

Implementing business process solutions in the way of Web service is being positioned in the center of workflow manag ement. However, there is no robust standard to expose and access workflow resources by Web service interfaces. In this paper, we propose a web service based workflow resource management framework named Universal Resource Manage ment Framework (URMF) with declarations of web service interfaces and interaction protocols among them. We also in troduce a substitutive workflow interface model employing Web services and URMF. Finally, a prototype implementati on model of URMF with J2EE platform is also introduced

Observation-Guided Diffusion Probabilistic Models

We propose a novel diffusion model called observation-guided diffusion probabilistic model (OGDM), which effectively addresses the trade-off between quality control and fast sampling. Our approach reestablishes the training objective by integrating the guidance of the observation process with the Markov chain in a principled way. This is achieved by introducing an additional loss term derived from the observation based on the conditional discriminator on noise level, which employs Bernoulli distribution indicating whether its input lies on the (noisy) real manifold or not. This strategy allows us to optimize the more accurate negative log-likelihood induced in the inference stage especially when the number of function evaluations is limited. The proposed training method is also advantageous even when incorporated only into the fine-tuning process, and it is compatible with various fast inference strategies since our method yields better denoising networks using the exactly same inference procedure without incurring extra computational cost. We demonstrate the effectiveness of the proposed training algorithm using diverse inference methods on strong diffusion model baselines

Concrete delamination depth estimation using a noncontact mems ultrasonic sensor array and an optimization approach

In this study, we present a method to estimate the depth of near-surface shallow delamination in concrete using a noncontact micro-electromechanical system (MEMS) ultrasonic sensor array and an optimization-based data processing approach. The proposed approach updates the bulk wave velocities of the tested concrete element by solving an optimization problem using reference ultrasonic scanning data collected from a full-depth concrete region. Subsequently, the depth of concrete delamination is estimated by solving a separate optimization problem. Numerical simulations and laboratory experiments were conducted to evaluate the performance of the proposed ultrasonic data processing approach. The results demonstrated that the depth of shallow delamination in concrete structures could be accurately estimated

Sleep, mood disorders, and the ketogenic diet: potential therapeutic targets for bipolar disorder and schizophrenia

Bipolar disorder and schizophrenia are serious psychiatric conditions that cause a significant reduction in quality of life and shortened life expectancy. Treatments including medications and psychosocial support exist, but many people with these disorders still struggle to participate in society and some are resistant to current therapies. Although the exact pathophysiology of bipolar disorder and schizophrenia remains unclear, increasing evidence supports the role of oxidative stress and redox dysregulation as underlying mechanisms. Oxidative stress is an imbalance between the production of reactive oxygen species generated by metabolic processes and antioxidant systems that can cause damage to lipids, proteins, and DNA. Sleep is a critical regulator of metabolic homeostasis and oxidative stress. Disruption of sleep and circadian rhythms contribute to the onset and progression of bipolar disorder and schizophrenia and these disorders often coexist with sleep disorders. Furthermore, sleep deprivation has been associated with increased oxidative stress and worsening mood symptoms. Dysfunctional brain metabolism can be improved by fatty acid derived ketones as the brain readily uses both ketones and glucose as fuel. Ketones have been helpful in many neurological disorders including epilepsy and Alzheimer’s disease. Recent clinical trials using the ketogenic diet suggest positive improvement in symptoms for bipolar disorder and schizophrenia as well. The improvement in psychiatric symptoms from the ketogenic diet is thought to be linked, in part, to restoration of mitochondrial function. These findings encourage further randomized controlled clinical trials, as well as biochemical and mechanistic investigation into the role of metabolism and sleep in psychiatric disorders. This narrative review seeks to clarify the intricate relationship between brain metabolism, sleep, and psychiatric disorders. The review will delve into the initial promising effects of the ketogenic diet on mood stability, examining evidence from both human and animal models of bipolar disorder and schizophrenia. The article concludes with a summary of the current state of affairs and encouragement for future research focused on the role of metabolism and sleep in mood disorders

Regulation of thyroid hormone-induced development \u3cem\u3ein vivo\u3c/em\u3e by thyroid hormone transporters and cytosolic binding proteins

Differential tissue sensitivity/responsivity to hormones can explain developmental asynchrony among hormone-dependent events despite equivalent exposure of each tissue to circulating hormone levels. A dramatic vertebrate example is during frog metamorphosis, where transformation of the hind limb, brain, intestine, liver, and tail are completely dependent on thyroid hormone (TH) but occurs asynchronously during development. TH transporters (THTs) and cytosolic TH binding proteins (CTHBPs) have been proposed to affect the timing of tissue transformation based on expression profiles and in vitro studies, but they have not been previously tested in vivo. We used a combination of expression pattern, relative expression level, and in vivo functional analysis to evaluate the potential for THTs (LAT1, OATP1c1, and MCT8) and CTHBPs (PKM2, CRYM, and ALDH1) to control the timing of TH-dependent development. Quantitative PCR analysis revealed complex expression profiles of THTs and CTHBPs with respect to developmental stage, tissue, and TH receptor β (TRβ) expression. For some tissues, the timing of tissue transformation was associated with a peak in the expression of some THTs or CTHBPs. An in vivo overexpression assay by tail muscle injection showed LAT1, PKM2, and CRYM increased TH-dependent tail muscle cell disappearance. Co-overexpression of MCT8 and CRYM had a synergistic effect on cell disappearance. Our data show that each tissue examined has a unique developmental expression profile of THTs and CTHBPs and provide direct in vivo evidence that the ones tested are capable of affecting the timing of developmental responses to TH

Addressing Negative Transfer in Diffusion Models

Diffusion-based generative models have achieved remarkable success in various domains. It trains a model on denoising tasks that encompass different noise levels simultaneously, representing a form of multi-task learning (MTL). However, analyzing and improving diffusion models from an MTL perspective remains under-explored. In particular, MTL can sometimes lead to the well-known phenomenon of $\textit{negative transfer}$, which results in the performance degradation of certain tasks due to conflicts between tasks. In this paper, we aim to analyze diffusion training from an MTL standpoint, presenting two key observations: $\textbf{(O1)}$ the task affinity between denoising tasks diminishes as the gap between noise levels widens, and $\textbf{(O2)}$ negative transfer can arise even in the context of diffusion training. Building upon these observations, our objective is to enhance diffusion training by mitigating negative transfer. To achieve this, we propose leveraging existing MTL methods, but the presence of a huge number of denoising tasks makes this computationally expensive to calculate the necessary per-task loss or gradient. To address this challenge, we propose clustering the denoising tasks into small task clusters and applying MTL methods to them. Specifically, based on $\textbf{(O2)}$, we employ interval clustering to enforce temporal proximity among denoising tasks within clusters. We show that interval clustering can be solved with dynamic programming and utilize signal-to-noise ratio, timestep, and task affinity for clustering objectives. Through this, our approach addresses the issue of negative transfer in diffusion models by allowing for efficient computation of MTL methods. We validate the proposed clustering and its integration with MTL methods through various experiments, demonstrating improved sample quality of diffusion models.Comment: 22 pages, 12 figures, under revie