High-density, high-permanence forms of carbon storage are in demand to save storage space on land or at sea while allowing the world to reach its climate targets. Biochar and calcium carbonate are two such forms that have been considered largely separately in the literature for carbon storage. In this paper, we consider how biochar and calcium carbonate might interact when they are used together with cement as part of a carbon storage system, ideally to form a carbon-neutral concrete. The carbon storage system stores atmospherically absorbed CO₂ within concrete, thereby reducing carbon in the atmosphere. In addition, such a system will help in reducing cement usage, thus reducing the need for clinker in cement manufacturing and directly reducing CO₂ emissions that result from limestone calcination during clinker manufacturing. Another benefit of such a composite storage system is its use in building structures, a use that has positive environmental and social impact. Thus, further research on the properties of this composite material is warranted. This paper explores the literature on the use of biochar combined with calcium carbonate and cement as carbon storage material. The use of recycled carbon aggregates (RCAs) and LC3 concrete as part of this approach is reviewed. The paper also addresses the possible compressive strength range of the biochar–cement–calcium carbonate composite material, along with other performance expectations. Obstacles to scaling the use of carbon-neutral concrete are identified and an array of research directions are presented, with the goal of improving carbon-neutral concrete and its use.

Cement manufacturing is known for its significant energy consumption and environmental footprint. As the world strives for sustainability, optimizing electrical energy consumption (EEC) in cement manufacturing is essential for reducing operational costs and minimizing the industry’s environmental impact. This systematic review aims to synthesize and analyze existing scholarly works and industry reports on methods and approaches for EEC optimization in cement production. It examines papers published between 1993 and 2023 in academic databases, scholarly journals, and industry publications to identify open questions and areas where future research may be needed. While challenges remain, continued research and innovation are key to further advancements in energy efficiency in cement production. With the advent of Industry 4.0 digitalization and advancements in data analytics and industrial Internet of Things (IIoT), artificial intelligence (AI) can be leveraged to optimize EEC. This study is a review of the applications of artificial intelligence to EEC optimization in industries that have heavy demand for electric power to highlight the value of directing research to its applications in cement manufacturing. The study posits that with digitalization, applying artificial intelligence to extract operational insights from the data collected from embedded sensors and meters at the plant presents the most cost-effective, high-return, and low-risk opportunity to optimize EEC in cement manufacturing.

A modified direct shear test apparatus was designed and used to measure cohesion and friction angle of rock pile materials. Two test apparatuses were constructed, a 30-cm square metal shear box and a 60-cm square metal shear box. In addition to the shear box, the testing apparatus has a metal top plate, a fabricated roller plate, normal and shear dial gages with wooden supports, and two hydraulic jacks and cylinders with a maximum oil pressure of 70 MPa (10,000 psi) and a load capacity of ten tons. The main difference between the in situ shear box and its conventional laboratory equivalent is that the in situ shear box consists of a single box that confines an excavated block of rock pile material. The lower half of the block consists of the rock pile material underneath the shear plane that is a semi-infinite domain. This modification in the shear test apparatus reduces the time needed for block preparation, helps perform several tests at different levels of the same sample block, and allows for accommodating large shear displacement with no reduction in the area of the shear plane.

This study uses machine learning methods to model different stages of the calcination process in cement, with the goal of improving knowledge of the generation of CO₂ during cement manufacturing. Calcination is necessary to determine the clinker quality, energy needs, and CO₂ emissions in a cement-producing facility. Due to the intricacy of the calcination process, it has historically been challenging to precisely anticipate the CO₂ produced. The purpose of this study is to determine a direct association between CO₂ generation from the manufacture of raw materials and the process factors. In this paper, six machine learning techniques are investigated to explore two output variables: (1) the apparent degree of oxidation, and (2) the apparent degree of calcination. CO₂ molecular composition (dry basis) sensitivity analysis uses over 6000 historical manufacturing health data points as input variables, and the results are used to train the algorithms. The Root Mean Squared Error (RMSE) of various regression models is examined, and the models are then run to ascertain which independent variables in cement manufacturing had the largest impact on the dependent variables. To establish which independent variable has the biggest impact on CO₂ emissions, the significance of the other factors is also assessed.

The cement and concrete industries face significant challenges in decarbonization due to escalating demand driven by rapid urbanization, population growth, and infrastructure restoration needs. Cement production alone accounts for 8% of global anthropogenic CO₂ emissions, underscoring the urgency of exploring pathways to achieve net-zero emissions in these sectors. With over 120 nations committing to net-zero targets by 2050, a comprehensive examination of emerging carbon-saving technologies is imperative. While several promising innovations are in nascent stages, rigorous life cycle assessments are essential to determine their potential for carbon reduction. Practical strategies for achieving net-zero objectives and UN sustainability goals involve embracing circular economy principles, harnessing diverse by-product sources, fostering stakeholder engagement, and fostering technological innovation. An efficiency approach that integrates advancements in materials science, alternative fuels, and sector-wide efficiencies is crucial for success. This assessment identifies promising technologies, highlights knowledge gaps, underscores the importance of further research, and offers recommendations for implementing best practices on the path to net zero.

Haul trucks generate more than half of direct greenhouse-gas emissions in typical mining operations, making haulage decarbonization central to the industry’s net zero commitments. Artificial intelligence (AI) is increasingly applied to haulage, but existing reviews examine individual technologies rather than linking them to decarbonization and ESG outcomes. This comprehensive review synthesizes 73 peer-reviewed sources from 2017 to 2026 across route optimization, electric and hybrid trucks, predictive maintenance, fleet monitoring, carbon footprint reduction, and ESG performance, grades reported outcomes by validation basis, and develops a framework linking AI-enabled technology domains, through their operational mechanisms, to ESG outcomes. Reinforcement-learning dispatching reduced haulage emissions by more than 30% relative to fixed scheduling in simulation, trolley assist reduced fuel use by up to 91%, and fleet electrification modeling projected GHG reductions of up to 92.6% in a hypothetical case, contingent on grid carbon intensity. Most reported reductions derive from simulation, and AI’s advantage over conventional control of electrified fleets remains undemonstrated. Gaps include scarce field-verified emission baselines, limited modeling of operator behavior, immature cybersecurity and data governance practice, and weak engagement between ESG research and haulage technology. AI-enabled operational measures offer a promising near-term decarbonization pathway that complements, rather than awaits, fleet electrification.