Use of sustainable chemistry approaches and ideas is essential for pursuit of sustainability in the chemical industry. Measuring progress is complicated by the existence of multiple issues and indicators across the life cycle of products. We developed and implemented an assessment process for the use of sustainable chemistry concepts by business units in our company, to encourage and track more sustainable practices and products. The annual process focuses on four broad themes: product risks, addressing world challenges through products and operations, strategy and recognition, and value chain engagement. It is also linked with elements of Dow’s 2025 Sustainability Goals. The process promotes dialogue, actions, and improvements by business units, and serves as a key mechanism for raising sustainability awareness in the company.

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The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. The first layer involves the γ and λ, values which are introduced to describe the current condition of characteristics of the searched solution that diversifies the particles when it is converging too much on some optima. The second layer involves the partitioning of particles that tries to prevent premature convergence. With the two search mechanisms, the PSPSO presents a simpler way of making the particles communicate with each other without too much compromise of the computational time. The proposed algorithm was compared with different variants of particle swarm optimization (PSO) using benchmark functions as well as the IEEE 10-unit unit commitment problem. Results proved the effectiveness of PSPSO with different functions and proved its competitive advantage in comparison with published PSO variants.

This paper investigates the feasible reserve of diesel generators in day-ahead unit commitment (DAUC) in order to handle the uncertainties of renewable energy sources. Unlike other studies that deal with the ramping of generators, this paper extends the ramp rate consideration further, using dynamic limits for the scheduling of available reserves (feasible reserve) to deal with hidden infeasible reserve issues found in the literature. The unit commitment (UC) problem is solved as a two-stage day-ahead robust scenario-based unit commitment using a metaheuristic new variant of particle swarm optimization (PSO) called partitioned step PSO (PSPSO) that can deal with the dynamic system. The PSPSO was pre-optimized and was able to find the solution for the base-case UC problem in a short time. The evaluation of the optimized UC schedules for different degrees of reserve consideration was analyzed. The results reveal that there is a significant advantage in using the feasible reserve formulation, especially for the deterministic approach, over the conventional computation in dealing with uncertainties in on-the-day operations even with the increase in the reserve schedule.

Different inverters types have different technical specifications and costs which contribute to the changes of inverter type decisions. It is therefore important to know which inverter is more advantageous for certain criteria. In this paper, the central, string, multi-string, and AC module types of inverters were compared through optimally designing a DG-PV and DG-PV-battery system of an off-grid island using each type of inverter. The problem was solved as a multi-objective optimization considering cost and uncertainty. The results were compared in terms of cost, reliability, renewable energy penetration, renewable energy curtailment, and changes in battery cost. The optimization results showed that the string inverter is best in terms of cost and highly affected by the decrease of the battery cost, the AC module in terms of reliability and curtailment, and the multi-string for renewable energy penetration. Meanwhile, the central inverter can be the choice when a balance between cost and reliability is considered.