In the field of mineral processing, controlling the paste thickener is a highly challenging and critical task because of the high complexity, incomplete observation space, and excessive environmental noises. In this article, we propose an offline-data-driven controlling strategy to optimize the operational indices in the thickening system based on offline reinforcement learning (RL). Compared to common RL methods that rely on online interactive training, our approach ensures the safety of the production process by training the controller solely using offline datasets, thereby avoiding dangerous online exploration. In terms of offline dataset collection, this study utilizes the prior knowledge of the thickening mechanism to design a proportional–integral–derivative controller as the behavior policy to collect operational trajectories as the offline dataset. In addition, to tackle a critical issue in controlling the thickening system: constrained observation space, this article analyzes the dynamical properties of the thickening system and introduces a novel offline RL algorithm, temporal batch-constrained Q-learning (TBCQ). The algorithm and associated model framework are specifically developed for controlling partially observed Markov decision processes. The TBCQ and trained policy are evaluated in both a simulated thickening environment and a real industrial paste thickener in a copper mine. The real-world experiments demonstrate that the proposed controller outperforms the baselines and effectively reduces the tracking error of underflow concentration by over 12%. The successful application of our pipeline in paste thickener also offers an innovative perspective on addressing optimization problems in complex industrial systems: performing offline RL on a dataset sampled from a suboptimal policy.

This paper explores the application of Cyber-physical Internet (CPI) in prefabricated construction logistics to enhance module shipment efficiency through a new scheduling framework. Drawing parallels between the TCP/IP model’s data transmission process and the physical shipment of construction modules, the study identifies inefficiencies in current logistics practices, including obstructed information sharing and collaboration among practitioners. To address these challenges, the paper proposes a suite of CPI protocols, like the internet protocols, to standardize information sharing, scheduling & routing rules among logistics practitioners and nodes. Based on the CPI protocols, a hierarchical and decentralized shipment decision framework is proposed to govern how the routing decisions and shipment scheduling decisions are made at each logistics node. A set of numerical experiments is conducted based on a real-life shipment case of construction modules in the Greater Bay Area to exhibit the great efficiency and resilience of the proposed protocol-based decision framework. And a case study is designed to show how the proposed protocols influence the decision process. The study’s contributions are threefold: demonstrating CPI application in a logistics scenario, developing protocols for efficient information sharing, and proposing a new decision framework for resilient and timely scheduling in complex logistics networks.

Root cause analysis (RCA) is a crucial technique in network systems for uncovering the abnormal nodes that lead to the network alarm flood. Within private cloud network systems, the calling chains and topologies among entities, such as hosts, routes, and services, are always incomplete due to nonstandardized management. Existing topology-free RCA techniques, which rely on the casual discovery, are inapplicable when the scale of the network system is extremely large or the number of triggered alarms is sparse. This article proposes NetEventCause (NEC), an event-driven, unsupervised, and nonintrusive RCA algorithm for large network systems, where the network topology is unknown. NEC learns from historical alarm events to model the occurrences of various alarm types using a multivariate neural temporal point process (TPP). Based on the conditional intensity predicted by the learned TPP, NEC can identify the root alarms from a cascade of alarm events and locate the causal alarms of derivative alarms using the attribution method. The experimental section evaluates the NEC using both a synthetic event dataset and a large real-world dataset. The real-world dataset is exported from the Huawei Shennong Intelligent Maintenance and Operation Center (IMOC), a platform deployed at one of China's largest airports and manages over 200000 entities. Results obtained from the two datasets demonstrate that NEC outperforms most state of the art (SOTA) TPP models in modeling alarm events and surpasses general RCA methods in terms of identifying root alarms and recovering transmission chains of anomalies.

For the complicated input-output systems with nonlinearity and stochasticity, Deep State Space Models (SSMs) are effective for identifying systems in the latent state space, which are of great significance for representation, forecasting, and planning in online scenarios. However, most SSMs are designed for discrete-time sequences and inapplicable when the observations are irregular in time. To solve the problem, we propose a novel continuous-time SSM named Ordinary Differential Equation Recurrent State Space Model (ODE-RSSM). ODE-RSSM incorporates an ordinary differential equation (ODE) network (ODE-Net) to model the continuous-time evolution of latent states between adjacent time points. Inspired from the equivalent linear transformation on integration limits, we propose an efficient reparameterization method for solving batched ODEs with non-uniform time spans in parallel for efficiently training the ODE-RSSM with irregularly sampled sequences. We also conduct extensive experiments to evaluate the proposed ODE-RSSM and the baselines on three input-output datasets, one of which is a rollout of a private industrial dataset with strong long-term delay and stochasticity. The results demonstrate that the ODE-RSSM achieves better performance than other baselines in open loop prediction even if the time spans of predicted points are uneven and the distribution of length is changeable. Code is availiable at https://github.com/yuanzhaolin/ODE-RSSM.

Periodic Jump processes commonly occur in complex industrial systems. As the systems vary dynamically between different stages, learning their dynamics in an unified model, so as to forecast and simulation accurately is challenging. In this study, we propose autonomous jump ordinary differential equation net (AJ-ODENet) to learn the continuous-time periodic jump system. The model consists of several Hierarchical ODENets (H-ODENets) and a stage transition predictor. Each H-ODENet is an advanced version of ordinary differential equations network to individually learn specific dynamics in each stage from irregularly sampled sequence data. The stage transition predictor realizes autonomous stage transition during open-loop simulation. Furthermore, an encoder–decoder framework built on AJ-ODENet is employed on a real cooling system of data center to simulate some variables in runtime. With multivariate data given, such as server power and environmental temperature, the model can simulate the working patterns as in reality, and the relative error of the predicted energy consumption is within 5%. Furthermore, based on the model, we infer the optimal cooling temperature settings under different heat loads. The simulation results indicate that 6%–25% of cooling energy consumption can be optimized.